Browse Topic: Fault detection

Items (211)

Implementation Of Machine Learning In Acoustics Source Detection by Leveraging Synthetic Sound Data Generation Approach

2024-26-021301/16/2024

E-Mobility and Low Noise IC Engines has pushed product development teams to focus more on sound quality rather than just focus on reduced noise levels and legislative needs. Further more qualification of products from a sound quality perspective from an end of line testing requirement is also a major challenge. End of line (EOL) NVH testing is key evaluation criteria for product quality with respect to NVH and warranty. Currently for subsystem or component level evaluation, subjective assessment of the components is done by a person to segregate OK and NOK components. As human factor is included, the process becomes very subjective and time consuming. Components with different acceptance criteria will be present and it’s difficult to point out the root cause for NOK components. In this paper, implementation of machine learning is done for acoustic source detection at end of line testing. To improve the fault detection an automated intelligent tool has been developed for subjective to

Shukle, Srinidhi, Iyer, Ganesh, Faizan, Mohammed

Material Recognition Technology of Internal Loose Particles in Sealed Electronic Components Based on Random Forest

01-17-02-0009

12/05/2023

Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material

Gao, Yajie, Wang, Guotao, Jiang, Aiping, Yan, Huizhen

Recognition Method for Electronic Component Signals Based on LR-SMOTE and Improved Random Forest Algorithm

01-17-01-0005

06/10/2023

Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data

Lv, Bingze, Wang, Guotao, Li, Shuo, Wang, Shicheng, Liang, Xiaowen

A Comparative Analysis of Fault Diagnosis by vibration signals for Critical Gear Components in Electric Vehicle Motor Testing Machines Using Machine Learning Algorithms

2025-01-004005/05/2023

Electric vehicles (EVs) represent the future of the automobile industry, with the drive motor being a crucial component in modern EVs. To ensure optimal performance, a motor testing machine is used to verify the essential parameters of these electrical motors. This machine regularly evaluates electric motor characteristic curves and their electromagnetic behavior under different conditions. However, it has been observed that flaws in the testing machine's helical gear arrangement frequently cause malfunctions and suboptimal performance, affecting the efficiency of the electric vehicle production line. This research project aims to improve the motor test bench apparatus by modifying critical design parameters using machine learning and vibration signal analysis methods. Vibration signals are recorded at various gear settings, and statistical features are extracted from these signals. These signals are then classified using classifiers such as Naive Bayes and Decision Trees. Machine

S PhD, Ravikumar, V, Muralidharan, Syed, Shaul

An Efficient Machine Learning Algorithm for Valve Fault Detection

2022-01-018503/29/2022

Multi-level Miller-cycle Dynamic Skip Fire (mDSF) is a combustion engine technology that improves fuel efficiency by deciding on each cylinder-event whether to skip (deactivate) the cylinder, fire with low (Miller) charge, or fire with a high (Power) charge. In an engine with two intake and two exhaust valves, skipping the cylinder is accomplished by deactivating all valves, while firing with a reduced charge is accomplished by deactivating one of the intake valves. This new ability to modulate the charge level introduces new failure modes. The first is a failure to reactivate the single intake valve, which results in a desired high-fire having the air intake of a low-fire; the second is a failure to deactivate the single intake valve, which results in a low-fire having the air intake of a high-fire. Reliably detecting these two faults has proven challenging for classical techniques that use measured MAP and/or crank angle acceleration to identify characteristic features of the

Serrano, Joe, Ortiz-Soto, Elliott, Chen, S Kevin, Chien, Li-Chun, Joshi, Abhishek

Multiple Engines Fault Detection Using Variational Mode Decomposition and GA-K-means

2022-01-074103/29/2022

As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The

Tang, Daijie, Bi, Fengrong, Yang, Xiao, Li, Xin, Shen, Pengfei, Tian, Congfeng

Fault Detection and Diagnosis of Engine Spark Plugs Using Deep Learning Techniques

03-15-04-002711/10/2021

Fault Detection and Diagnosis (FDD) is playing an increasingly important role in the automotive sector as it moves toward Advanced Technology Vehicles. Reducing the cost of sensory equipment to detect faults in Internal Combustion Engines (ICEs) has always been a common desire for automotive researchers. This article offers an Artificial Intelligence approach for detecting engine combustion faults related to spark plugs using existing sensors. The study investigates two deep learning models that are capable of learning different fault conditions from historical sensory data. The two customized models, one Long Short-Term Memory (LSTM) neural network and one Convolutional Neural Networks (CNN) model, are proposed to tackle this task. The LSTM model processes the filtered sensor data in time series, while the CNN model uses the frequency map that is novel in the learning-based engine diagnosis field. A comprehensive engine fault dataset is collected and includes a variety of operating

Huangfu, Yixin, Seddik, Essam, Habibi, Saeid, Wassyng, Alan, Tjong, Jimi

Fault diagnosis of ball bearings using machine learning of vibration signals

2021-28-017810/01/2021

One of the major reasons for the failure of rotating machines are rolling element bearing defects. Failure of bearings leads to unplanned maintenance shutdowns and unsafe working conditions. For these reasons, it is very important to detect and identify the defects in rolling element bearings in its early stage. Vibration signals are well known for monitoring the conditions of rotating machineries. The performance of conventional intelligent fault diagnosis methods depends on feature extraction of vibration signals, which requires signal processing techniques, good proficiency, and human expertise. Recently, deep learning algorithms have been applied widely in machine health monitoring. Here in this study, a machine learning based model for the detection of bearing defects is analysed. The bearings used for this analysis is 6305 deep groove ball bearing. Defects like ball defect, outer race defect, and inner race defect were considered. A motor driven variable speed test rig rotor

Poulose, Jasmine, prasad SR, Vishnu, Sadique, Anwar

Vibration analysis of Gear defects using Machine learning approach

2021-28-018210/01/2021

Gear drives are considered as the most effective transmission method in automobiles as well as in various industries because of its high efficiency, reliability and high velocity ratio. As a result, from its trustful usage, failure in any part may lead to a large and unpredictable production loss along with massive service cost and safety concerns. Periodic maintenance and condition monitoring are the only solution to avoid the above scenario. Vibration analysis are most sounded term in the fault detection due to its runtime condition monitoring and low cost. Nowadays, vibration analysis was off set to machine learning methods, which is a modern technique enable us the automation such that the system can learn from the input data and make decision with a nominal human interface were as conventional methods are highly operator dependent. Here in this study, the effectiveness of a machine learning based gear fault diagnosis system is studied. Gear defects like surface pitting

prasad SR, Vishnu, Poulose, Jasmine, Sadique, Anwar

Design, Development and Test Criteria - Solid State Proximity Switches/Systems for Landing Gear Applications

AIR1810C (Current)02/03/2021

This document examines the most important considerations relative to the use of proximity sensing systems for applications on aircraft landing gear. In general, the recommendations included are applicable to other demanding aircraft sensor installations where the environment is equally severe.

A-5B Gears, Struts and Couplings Committee

“Rds_on” Based OBD for Pre-Supply Fuel Pump Driver Modules

SAE-PP-0016001/26/2021

In automotive electronics on-board diagnostics does the fault diagnosis and reporting. It provides the level of robustness required for the control electronics against various faults. The amount of diagnostic information available via on board diagnostics are depends on the type of vehicle. Pre-supply fuel pump is the component in the common rail hydraulic system. It pumps the fuel from the fuel tank to the inlet valve of the high pressure fuel pump. Electronic control unit synchronizes its operation with high pressure fuel pump. A dedicated driver module in the ECU controls the operation of pre-supply fuel pump. The driver module consist of an ASIC with internal voltage, current monitoring modules for the fault diagnosis and the pre-drivers to control external HS and LS power stages. The software part of the OBD programmed in the internal memory of the ASIC. The “Rds_on” of the power MOSFETs are used for the fault detection purpose. The module designed to operate in dual frequencies

MobrxivNonAdmin, Lindsay

Machine Health & Asset Monitoring in Industrial Applications: A Look at Sensor Technologies

TBMG-3814012/01/2020

The data gained from monitoring remote equipment is critical to the functionality of any industrial process. Often, this data is handled by a Supervisory Control and Data Acquisition (SCADA) control system often via an Ethernet and TCP/IP network over a bus, star, or tree topology. Industrial Internet of Things (IIoT) systems are often augmenting and in some cases, replacing these legacy systems to allow for a wireless network of nodes connected to a gateway that leads back to the cloud for more complex data processing and analytics. Regardless of the use of wired or wireless technologies, the underlying sensors used in these processes provide the backbone for the data required for assessing and analyzing plant equipment.

Modern Battery Systems – the eMobility Enabler Virtual Pre-Conference Certification

C201708/07/2020

eMobility presents enormous challenges to engineers who have been engaged primarily in non-electrical sectors of vehicle engineering. Yet, eMobility also presents exciting opportunities to those who are ready to understand how high voltage batteries contribute to the success of eMobility. This seminar will bring together all the materials in an easy to follow format geared to a wide range of participants from engineers to technicians to sales staff to executives. To grasp the opportunities, we need to understand a small number of topics that describe how a battery system contributes to eMobility applications. However, that is only one aspect of how to develop a successful product. As the history of automotive engineering has demonstrated in the marketplace time after time, if the customer is not satisfied with the product, success will be elusive. This seminar will take us, in a team-based problem-solving session, through what we think the customer needs and wants are. As importantly

Directional UAV Localization of Power Line Ultraviolet Corona

TBMG-3748308/01/2020

NASA’s Langley Research Center has developed a novel system that uses an ultraviolet camera to detect, inspect, and analyze a corona discharge. This discharge signifies a power line fault, making the technology ideal for use in power line inspections. When coupled to a drone, the technology offers the ability to remotely monitor power lines in a cost-effective way. Adding GPS technology results in precise location of power line faults.

Automated Diagnosis of Engine Misfire Faults Using Combination Classifiers

02-13-02-0007-TEST-REC06/26/2020

Existing on-board diagnostics vehicle systems can detect the existence of faults, but their diagnostic (fault isolation) capabilities are rather low. Extensions to on-board diagnostics are needed in order to provide a high degree of automated diagnostic support. In this context, we study in this article the problem of internal combustion engine misfires, which constitute a class of automotive faults known to be difficult to diagnose, and present a combination classifier that has excellent performance in classifying the various root causes of misfire faults. We first obtained real-life data and built a database consisting of 2,299 time instances of actual misfire and misfire-free cases. Fault data were captured on several different vehicle makes and models, with each misfire fault belonging to one of three different categories (air-intake, coil-ignition, and fuel-injection), further subdivided into a total of seven subcategories. We then developed a combination classifier (referred to

Suda, Jessica L., Kagaris, Dimitri

Automated Diagnosis of Engine Misfire Faults Using Combination Classifiers

02-13-02-0007

06/26/2020

Suda, Jessica L., Kagaris, Dimitri

Fault Diagnosis of an Engine through Analyzing Vibration Signals on the Block

2020-01-156806/03/2020

Unpredictable faults oriented from ambiguous reasons could occur in an engine of a vehicle. However, there are some symptoms from which an engine is working abnormally before the engine is stalled by faults. In this paper, methods for diagnosis of engine faults by using vibrations are proposed. Through bench tests, to extract features for fault diagnosis, various samples with normal and abnormal conditions are prepared and vibration signals from the block of an engine are measured and analyzed. To consider cost and performance of a sensor, vibrations from a knock sensor signal as well as accelerometers are analyzed. Measured vibration signals are synchronized with signal of the crank position sensor and analyzed to detect which event is involved. Modulation analysis and Hilbert transform are applied to extract features representing the symptoms of engine faults and to indicate when the abnormal event happens, respectively. As a result, the mean value of modulation indexes at modulated

Jin, Jaemin, Jung, Insoo, Shin PhD, Sung-Hwan

Bearing Fault Diagnosis of the Gearbox Using Blind Source Separation

2020-01-043604/14/2020

Gearbox fault diagnosis is one of the core research areas in the field of rotating machinery condition monitoring. The signal processing-based bearing fault diagnosis in the gearbox is considered as challenging as the vibration signals collected from acceleration transducers are, in general, a mixture of signals originating from an unknown number of sources, i.e. an underdetermined blind source separation (UBSS) problem. In this study, an effective UBSS-based algorithm solution, that combines empirical mode decomposition (EMD) and kernel independent component analysis (KICA) method, is proposed to address the technical challenge. Firstly, the nonlinear mixture signals are decomposed into a set of intrinsic mode function components (IMFs) by the EMD method, which can be combined with the original observed signals to reconstruct new observed signals. Thus, the original problem can be effectively transformed into over-determined BSS problem. Then, the whitening process is carried out to

Zhong, Hong, Liu, Jingxing, Wang, Liangmo, Ding, Yang, Qian, Yahui

Proton Exchange Membrane Fuel Cell Fault Rapid Diagnosis Method Based on Electrochemical Impedance Spectroscopy and Fuzzy C-Means Algorithm

2019-01-503211/04/2019

Water management is a key research direction for the performance and lifetime of proton exchange membrane fuel cell (PEMFC) stacks. The paper is aimed to develop an online fault diagnosis method that distinguishes different degrees of flooding and drying within a fuel cell stack by unobservable variables. In our research, the equivalent circuit model is established and electrochemical impedance spectroscopy (EIS) is utilized. The mathematical methods are used to extract the fault features. Fuzzy C-means is used to classify the selected features and the diagnostic rules are automatically extracted from the data. Through verification, the interpretability and computational efficiency of the proposed method are achieved.

Zhou, Su, Zheng, Shoujian, Hu, Zhe

2019 'Create the Future' Electronics/Sensors/IOT Category Winner: Early Detection of Battery Faults

TBMG-3544711/01/2019

Software Applications for the Control and Management of the Amine Swingbed Experiment

TBMG-3526510/01/2019

The Swingbed software applications provide for the control, command, fault detection, fault recovery, and telemetry monitoring aspects of the Amine Swingbed experiment. These software components are the Swingbed Loader Computer Software Configuration Item (CSCI), the Swingbed Control Module, and the Swingbed Ground Controller applications. As a whole, the Amine Swingbed experiment provides a means for investigating the removal of carbon dioxide from the International Space Station (ISS) crews’ breathing environment via a system of a vacuum-regenerated amine pressure swing absorption reaction beds. Its development and deployment aboard the ISS as an Express Rack pay-load serves to advance the use of the amine-based pressure swing absorption technology towards a level of technology readiness suitable for use in future space transportation systems, where the use of consumables for the removal of carbon dioxide from the breathable environment is not desirable.

The Right Stuff for Aging Electronics/Intermittence/No Fault Found

2019-01-188909/16/2019

For those in the avionics repair and maintenance business, the acronyms NFF (No Fault Found), NTF (No Trouble Found), and CND (Cannot Duplicate) are, unfortunately, all too familiar terms. After several decades of frustration with this illusive phenomenon, it continues to consume an enormous amount of test and diagnostic effort and is the source of considerable cost and discomfort within the multi-level avionics repair model. There are undoubtedly many causes of NFF and all of them should be addressed. The question is: Where do you start and which solution will be the most beneficial? Our particular efforts have focused on the literal or statistical analysis of NFF, recognizing that if the system’s MTBF (Mean Time Between Failure) has decreased, or if the device's NFF rate has increased with age and deterioration, a physical fault is most likely present. However, if it isn’t found during conventional testing then it probably only fails intermittently. Similarly, having an intermittent

Knudsen, Hector I.

Safety Analysis: The Key to a Single-Pass ISO26262 Random Fault Workflow

TBMG-3462906/01/2019

Advanced driver-assistance systems and autonomous drive technologies increase the complexity of automotive integrated circuits (ICs), making it harder to ensure that ICs are protected from random hardware faults. Safety mechanisms must be inserted to identify and control these unpredictable functional failures, and ISO26262 requires that the effectiveness of every safety mechanism is proven.

Study of Replacing the Traditional Electromechanical Relay with the Full Semiconductor Solution of Bussed Electrical Center

2019-01-048404/02/2019

To face the challenges of CO2 emission and automated driving, the electrical distribution system (EDS), as the basis of all electronic loads, needs to be continuously changed. Traditional bussed electrical center (BEC) has limited functions such as simple switch and fuse protection, while the full semiconductor solution of smart BEC can provide more accurate diagnosis, faster response, higher reliability with lower power loss and smaller space. This paper will introduce the practical function of the smart BEC: in normal operation of the car, the voltage and current of the loads can be detected by the smart BEC. Once in abnormal, immediate feedback will be transferred from smart BEC to the whole system and a related response will be triggered in time, while the cost of power harness can also be optimized. In parking mode, the quiescent current of the loads from KL30 can be detected by smart BEC, which could prevent against leakage. Automated driving is a hot topic, many people focus on

Tian, Xia, Shen, Ning, Wang, Xingwei

Battery Management System Based on AURIX Multi-Core Architecture

2019-01-131004/02/2019

Battery management system (BMS) is the core component of the new energy vehicle battery system. With the increase of energy density of new energy vehicle battery, its control algorithm becomes more and more complex, and the work of the battery management system will be heavier. In order to solve the limits, the hardware, software and control strategy model of battery management system are developed based on AURIX multi-core microcontroller. The microprocessor control unit is developed by using dual-core chip, which meets the functional safety requirements. Dual-core processing of control strategy and individual information acquisition are realized, and the processing efficiency is improved. A four-tier software architecture of battery management system is developed to handle the Dual-core processing. The graphical development of battery management system strategy model is realized by using MATLAB / Simulink. The results of bench test and real vehicle test show that the developed

Huang, Denggao, Zhu, Zhongwen, Zhao, Jing, Wang, Xu, Tian, Yongjun

A Fault Tolerant Time Interval Process for Functional Safety Development

2019-01-011004/02/2019

During development of complex automotive technologies, a significant engineering effort is often dedicated to ensuring the safe performance of these systems. An important aspect to consider when assessing the viability of different safety designs or strategies is the time period from the occurrence of a fault to the violation of a Safety Goal (SG). This time period is commonly referred to as the Fault Tolerant Time Interval (FTTI). In Automotive Safety, ISO 26262 [1] calls for the identification and appropriate partitioning of the FTTI, however very little guidance is provided on how to do this. This paper presents a process, covering the entire safety development lifecycle, for the identification of timing constraints and the development of associated requirements necessary to prevent Safety Goal violations.

Denomme, Daniel, Hooson, Sam, Winkelman, James

Regression Techniques for Parameter Estimation of a Synchronous Machine from Sudden Short-Circuit Testing

2019-01-135403/19/2019

A sudden short-circuit (SSC) laboratory test of an electric machine is a commonly used procedure to estimate model parameters that accurately represent the dynamic response of the machine. While the graphical interpretation of the short-circuit current is often discussed in great detail, the numerical methods used to determine the solution for the machine parameter estimation is a challenging proposition. In this paper, the authors present an integral regression technique to fit the characteristic equation of the short-circuit current to a curve that is composed of exponential decays that trail off to an unknown steady-state value in the presence of noise. The proposed estimation method is applied to laboratory data from an aerospace synchronous machine.

Robbins, Brett A., Perdikakis, Will, Yost, Kevin J.

Self-Stabilizing, Distributed, Symmetric, Fault-Tolerant Synchronization

TBMG-3392303/01/2019

Distributed systems have become an integral part of safety-critical computing applications, necessitating system designs that incorporate complex fault-tolerant resource management functions to provide globally coordinated operations with ultra-reliability. As a result, robust clock synchronization has become a required fundamental component of fault-tolerant, safety-critical distributed systems. Since physical oscillators are inherently imperfect, local clocks of nodes of a distributed system, driven by these oscillators, do not keep perfect time and can drift with respect to real time and one another. Thus, the local clocks of the nodes must periodically be re-synchronized. As a result, a fault-tolerant system needs a clock synchronization algorithm that tolerates imprecise local clocks and faulty behavior by some processes.

Smart Actuators Deliver on the Promise of Industry 4.0

TBMG-3335712/01/2018

As Industry 4.0 initiatives bring more and more industrial axes of motion into the realm of automation, the need for cost-effective control across them grows as well. Advances in robotics, connectivity, cloud computing, artificial intelligence, data analysis, mobility, and numerous other areas are converging to push global industry to new plateaus of operational efficiency and creating roles for automated actuators in places previously thought impractical.

DC Arc Fault Detection Methods in MEA Distribution Systems

2018-01-193410/30/2018

Direct current (DC) for primary power distribution is a promising solution that is being explored by aircraft system integrators for MEA applications to enable the paralleling of non-synchronized engine off-take generators, and to enable the reduction of energy conversion stages required to supply electronically actuated loads. However, a significant challenge in the use of DC systems is the reliable detection of arc faults. Arcing presents a significant fire risk to aircraft and their presence can result in critical system damage and potentially fatal conditions. Series arc faults in DC systems are particularly challenging to detect as the associated reduction in system current eliminates the use of conventional overcurrent and current differential methods for fault detection. This paper provides an overview of series arc faults in DC systems and presents both simulation and hardware results to illustrate key trends, characteristics and discriminating features. It also presents a

Thomas, Jeffy, Telford, Rory, Rakhra, Puran, Norman, Patrick, Burt, Graeme

ARC FAULT CIRCUIT BREAKER - AIRCRAFT, TRIP FREE, 25 TO 35 AMP, TYPE 1

AS5692/4 (Current)09/02/2018

AE-7P Protective and Control Devices

ARC FAULT CIRCUIT BREAKER - AIRCRAFT, TRIP FREE, 1 TO 20 AMP, TYPE I

AS5692/3 (Current)09/02/2018

AE-7P Protective and Control Devices

Power Outlet Analyzes Electrical Current Usage

TBMG-3247908/01/2018

Electrical interruptions, or “nuisance trips,” occur when a detector installed behind the wall trips an outlet's electrical circuit when it senses something that could be an arc-fault — a potentially dangerous spark in the electric line. The problem with today's arc-fault detectors is that they often err on the side of being overly sensitive, shutting off an outlet's power in response to electrical signals that are actually harmless.

An Online Fault Detection and Isolation Method for Permanent Magnet Synchronous Machine

2018-01-045104/03/2018

An online fault detection and isolation (FDI) method for several common sensor faults and even demagnetization of PMSM is proposed by combining model-based and signal analysis technology. To begin with, the field reconstruction method (FRM) of PMSM is employed to obtain the flux residuals which are used as the criterion of fault detection. Then, the flux residuals are transformed by multi sequence harmonic synchronous rotating transformation and inputted into low pass filters (LPFs) in order to obtain the DC components. Last, offset and gain faults of the two phase current sensors, offset fault of the rotor angle sensor and permanent magnet (PM) demagnetization can be isolated by comparing the DC components and preset thresholds. The detection and isolation strategy of PMSM is validated by motor controller hardware in motor bench tests.

Zhong, Zaimin, Zhou, Shuihua, Chen, Xueping, Hu, Chengyu

Soil Moisture Active-Passive Project Spacecraft Flight Software

TBMG-2816001/01/2018

The Soil Moisture Active-Passive (SMAP) Project Spacecraft Flight Software controls all aspects of command and data handling (CDH) in the SMAP spacecraft. Required capabilities include uplink and command, telemetry and downlink, vehicle attitude control, science instrument control, onboard science data storage and downlink, spacecraft deployments, and spacecraft fault detection and response.

Designing With MIL-STD-1553B Components

TBMG-2793812/01/2017

The interconnected backbone of a MIL-STD-1553B vehicle depends on the electrical and mechanical reliability of the components, the design, and the installation. With the vast array of MIL-STD-1553B specified sub-assemblies, there are factors that can go overlooked for passive components such as couplers, cables, and connectors. Often built with kilometers of wiring, aircraft in particular can be difficult to troubleshoot and must be inspected frequently for potential failures such as intermittent connections, shorts, and corrosion. Still, these passive components that make up the network can be individually assessed to operate with a high level of integrity.

Integration of an End-of-Line System for Vibro-Acoustic Characterization and Fault Detection of Automotive Components Based on Particle Velocity Measurements

2017-01-176106/05/2017

The automotive industry is currently increasing the noise and vibration requirements of vehicle components. A detailed vibro-acoustic assessment of the supplied element is commonly enforced by most vehicle manufacturers. Traditional End-Of-Line (EOL) solutions often encounter difficulties adapting from controlled environments to industrial production lines due the presence of high levels of noise and vibrations generated by the surrounding machinery. In contrast, particle velocity measurements performed near a rigid radiating surface are less affected by background noise and they can potentially be used to address noise problems even in such conditions. The vector nature of particle velocity, an intrinsic dependency upon surface displacement and sensor directivity are the main advantages over conventional solutions. As a result, quantitative measurements describing the vibro-acoustic behavior of a device can be performed at the final stage of the manufacturing process. This paper

Fernandez Comesana, Daniel, Carrillo Pousa, Graciano, Tijs, Emiel

Model-Based Adaptive Fault Diagnosis in Lithium Ion Batteries: A Comparison of Linear and Nonlinear Approaches

2017-01-119203/28/2017

In this paper, multiple-model adaptive estimation techniques have been successfully applied to fault detection and identification in lithium-ion batteries. The diagnostic performance of a battery depends greatly on the modeling technique used in representing the system and the associated faults under investigation. Here, both linear and non-linear battery modeling techniques are evaluated and the effects of battery model and noise estimation on the over-charge and over-discharge fault diagnosis performance are studied. Based on the experimental data obtained under the same fault scenarios for a single cell, the non-linear model based detection method is found to perform much better in accurately detecting the faults in real time when compared to those using linear model based method.

Sidhu, Amardeep, Izadian, Afshin, Anwar, Sohel

Modular Fault Diagnosis System for Engine Test Bed Measurements

2017-01-038603/28/2017

To achieve high power output and good efficiency and to comply with increasingly stricter emission standards, modern combustion engines require a more complex engine design, which results in a higher number of control parameters. As the measurement effort and the number of sensors for engine development at the test bed continue to increase, it is becoming nearly impossible for the test bed engineer to manually check measurement data quality. As a result, automated methods for analysis and plausibility checks of measurement data are necessary in order to find faults as soon as they occur and to obtain test results of the highest possible quality. This paper presents a methodology for automated fault diagnosis on engine test beds. The methodology allows reliable detection of measurement faults as well as the identification of the root cause of faults. This methodology is based on a modular concept that combines several modules and methods consisting of physical principles as well as

Wohlthan, Michael, Pirker, Gerhard, Wimmer, Andreas

HyDE Model-Based Diagnosis Engine for Stochastic Hybrid Systems

TBMG-2637002/01/2017

Model-based diagnosis deals with the problem of diagnosing faults in systems using a model of the system for guidance. This problem is complicated by the presence of hybrid dynamics in the system (continuous evolution of the system interspersed with discrete events like commands to change configuration), as well as uncertainties in the form of model approximations and sensor noise. Several model-based technologies have been developed and successfully demonstrated using discrete abstractions of the system as models. These techniques are severely restricted in model expressiveness due to the discrete nature of the models. Moreover, sophisticated model abstraction techniques, as well as algorithms to convert continuous data to discrete form, need to be developed for such an approach to work. Recently, there have been efforts to develop diagnostic engines for hybrid and stochastic systems. However, these techniques have either focused on parametric faults, or use a probabilistic approach

PLCs Improve Control of Radio Broadcasts

TBMG-2643502/01/2017

Broadcasting employs the latest technology in many areas, but controlling the broadcast hardware connected to the antenna and the related transmission power, source, and facilities is usually still a low-tech operation. Typically, a broadcast engineer will drive to the unattended transmitter, translator, or repeater site to flip switches and make other manual adjustments (Figure 1).

“Rds_on” Based OBD for Pre-Supply Fuel Pump Driver Modules

2017-26-034801/10/2017

Mathew, Renny

Design of Double Layer Rectenna Array for Fault Isolation of Schottky Diode in Operation Beyond V-Band Frequencies

TBMG-2483006/01/2016

Microwave power transmission using rectenna technology has attracted a strong interest in conjunction with wireless electric power delivery to infrastructure and subjects located at a remote place. A typical rectenna, which is a major component of the wireless power transmission technology, consists of an antenna, a Schottky diode, and low-pass filters for low-frequency electromagnetic wave isolation in the device. To obtain high efficiency, an electromagnetic wave is collected through a high-resonance antenna, and the AC mode of coupled wave energy is delivered to a Schottky diode that rectifies AC power into DC power. By connecting rectennas in series or parallel, or in mixed way — as well as enlarging the receiving area — the rectenna array can capture microwave energy into a desirable mode of high power.

Ultrasound for Crimp Inspection

2016-01-010404/05/2016

In copper wire, real time crimp monitoring has traditionally been based on force measurement during the crimp cycle. The force attributed to molding the copper wire into the terminal is a significant portion of the total force needed to form the crimp. Therefore, any wire deviation from the norm is translated into a force pattern aberration that can be detected using basic signal pattern analysis. As the mobility industry is contemplating replacing copper with aluminum wire, in order to save on weight and material cost, the traditional force monitoring becomes ineffective in detecting wire faults in the crimp. The reason is that aluminum is softer than copper, and most of the force exerted during the crimp cycle is consumed by forming the copper terminal itself. The small force deviation due to an aluminum wire fault becomes much more difficult to detect. Therefore, a new technique is needed to monitor crimped aluminum wires. In this paper, we investigate the feasibility of using

Maalouf, Khalil, Stull, David, Nicholas, Keith

Conceptualization and Implementation of a Dual-Purpose Battery Electric Powertrain Concept for an Urban Utility/Activity Vehicle

2016-01-118204/05/2016

The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the sixth generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development and implementation of a dual-purpose powertrain system enabling vehicle propulsion as well as stationary activities of the Deep Orange 6 vehicle concept. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish vehicle target and subsystem requirement, which formed the foundation of the Unique Selling Points (USPs) of the concept. The Deep Orange 6 vehicle contains a very low floor

Ivanco, Andrej, Mariappan Selvaraj, Balan, Murali, Kawshik, Narayanan, Arjun, Sarkar, Avik, Singh, Aviral, Soni, Akshay, Benton, Mark, Muralidharan, Prasanth, Brooks, Johnell, Venhovens, Paul, Payne, Craig

Reducing Automotive Wire Harness Design Time and Cost Using Simulation

2016-01-010604/05/2016

One of the many critical design criteria for vehicle harness design is circuit protection. This process typically involves calculating the maximum load on each wire manually and then comparing the result to a spreadsheet that may be quite old. Testing physical prototypes occurs so late in the design process that problems found can be very expensive to rectify. Using simulation to detect faults, such as short circuits or the time for the fuse to blow vs. the time for the wire to smoke is an effective solution that can not only save a great deal in costs, but shorten the development cycle as well.

Stamper, Michael

Outlier Detection Via Estimating Clusters

TBMG-2385802/01/2016

The Outlier Detection Via Estimating Clusters (ODVEC) software provides an efficient method for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring. ODVEC uses models automatically derived from archived system data to identify unusual, out-of-family data samples (outliers) that indicate possible system failure or degradation. It employs novel techniques to efficiently calculate the degree of deviation of current system behavior from a range of previous, similar nominal operations.

Fault Recovery for Multi-Phase Power Converters

TBMG-23241

11/01/2015

A fault recovery method for multiphase power converters enables delivery of reduced output power of as much as 66% of normal power in the event of a shorted power switch component. The need for redundant power converters in conventional multi-phase space power systems is reduced, if not eliminated. Fault recovery includes detecting a shorted power switch fault, providing short circuit current protection, providing isolation of the shorted power switch, and reconfiguring the remaining undamaged power switches.

Modeling Space Operations Systems Using SysML as to Enable Anomaly Detection

2015-01-238809/15/2015

Although a multitude of anomaly detection and fault isolation programs can be found in the research, there does not appear to be any work published on architectural templates that could take advantage of multiple programs and integrate them into the desired systems. More specifically, there is an absence of a methodological process for generating anomaly detection and fault isolation designs to either embed within new system concepts, or supplement existing schemes. This paper introduces a new approach based on systems engineering and the System Modeling Language (SysML). Preliminary concepts of the proposed approach are explained. In addition, a case study is also mentioned.

Rabelo, Luis, Clark, Tom

Application of Order Analysis for Gear Fault Diagnosis under Variable Speed Conditions

2015-24-253009/06/2015

A vehicle gearbox serves for torque and speed conversion with help of rotating elements. Therefore the gearbox experiences periodic excitation forces with a fundamental frequency following the rotation frequency. These excitation forces give rise to corresponding periodic response signals, i.e. signals having content at the fundamental (rotational) frequency and its harmonics. Order analysis is an analysis technique which is used to extract these harmonic orders from the response signals. This article intends to use the order tracking analysis for gearbox fault diagnosis under variable speed conditions to compare between healthy and faulty cases by using order extraction. Finally, determine maximum Root Mean Square (RMS) as severity index. All the experimental data are acquired from vehicle gearbox which is tested on the laboratory stand equipped with three dynamometers; the input dynamometer serves as internal combustion engine, the output dynamometers introduce the load on the

El Morsy, Mohamed, Achtenova, Gabriela

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