Erratum: Single-fault diagnosis of nonlinear resistive networks

Engine ignition patterns can be analyzed to identify the engine fault according to both the specific prior domain knowledge and the shape features of the patterns. One of the challenges in ignition system diagnosis is that more than one fault may appear at a time. This kind of problem refers to simultaneous-fault diagnosis. Another challenge is the acquisition of a large amount of costly simultaneous-fault ignition patterns for constructing the diagnostic system because the number of the training patterns depends on the combination of different single faults. The above problems could be resolved by the proposed framework combining feature extraction, probabilistic classification, and decision threshold optimization. With the proposed framework, the features of the single faults in a simultaneous-fault pattern are extracted and then detected using a new probabilistic classifier, namely, pairwise coupling relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is not necessary. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnoses and is superior to the existing approach.

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Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

Mathematical Problems in Engineering ◽

10.1155/2013/827128 ◽

2013 ◽

Vol 2013 ◽

pp. 1-14 ◽

Cited By ~ 12

Author(s):

Zhixin Yang ◽

Pak Kin Wong ◽

Chi Man Vong ◽

Jianhua Zhong ◽

JieJunYi Liang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Gas Turbine ◽

Diagnostic System ◽

Kernel Principal Component Analysis ◽

Training Dataset ◽

Decision Threshold ◽

Turbine Generator ◽

Single Fault ◽

Probabilistic Classifier

A reliable fault diagnostic system for gas turbine generator system (GTGS), which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

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A CAD-Based Approach to Failure Diagnosis of CMOSLSI with Single Fault Using Abnormal IDDQ

ISTFA 1997: Conference Proceedings from the 23rd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa1997p0015 ◽

1997 ◽

Author(s):

M. Sanada

Keyword(s):

Fault Diagnosis ◽

Fault Location ◽

Logic Gates ◽

Physical Damage ◽

Logic Simulation ◽

Single Fault ◽

Circuit Structure ◽

Logic State ◽

Simple Logic ◽

Diagnosis Technique

Abstract A CAD-based fault diagnosis technique for CMOS-LSI with single fault using abnormal IDDQ has been developed to indicate the presence of physical damage in a circuit. This method of progressively reducingthe faulty portion, works by extracting the inner logic state of each block from logic simulation, and by deriving test vector numbers with abnormal IDDQ. To easily perform fault diagnosis, the hierarchical circuit structure is divided into primitive blocks including simple logic gates. The diagnosis technique employs the comparative operation of each primitive block to determine whether one and the same inner logic state with abnormal IDDQ exists in the inner logic state with normal IDDQ or not. The former block is regarded as normal block and the latter block is regarded as faulty block. The fault of the faulty block can be localized easily by using input logic state simulation. Experimental results on real faulty LSI with 100k gates demonstrated rapid diagnosis times of within ten hours ani reliable extraction of the fault location.

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Dynamic asymptotic model of rolling bearings with a single fault in the inner raceway

Advances in Mechanical Engineering ◽

10.1177/16878140211052288 ◽

2021 ◽

Vol 13 (10) ◽

pp. 168781402110522

Author(s):

Yunlong Li ◽

Zhinong Li ◽

Danyang Tian ◽

Junyong Tao

Keyword(s):

Fault Diagnosis ◽

Dynamic Analysis ◽

Reliability Analysis ◽

Dynamic Characteristics ◽

Calculation Method ◽

Asymptotic Model ◽

Rolling Bearings ◽

Single Fault ◽

Theoretical Support ◽

Rolling Element

In the previous models of rolling bearings with a single fault, the displacement deviation caused by the collision of the fault to the rolling element changes instantly. However, the displacement deviation should change gradually. Here, the asymptotic idea is introduced to describe the change of the displacement deviation. The calculation method of the deviation is given. An asymptotic model of rolling bearings with an inner raceway fault is constructed. Then, the simulation of the SKF6205 bearing with a single fault is carried out. The differences between the previous model and the asymptotic model for the responses and the displacement deviation are compared. The effects of the speed and fault size on the dynamic characteristics are analyzed. Finally, the experiments are carried out to corroborate the rationality of the constructed model. The research results can provide theoretical support for the dynamic analysis, fault diagnosis, and reliability analysis of rolling bearings.

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Intelligent Detection of a Planetary Gearbox Composite Fault Based on Adaptive Separation and Deep Learning

Sensors ◽

10.3390/s19235222 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5222 ◽

Cited By ~ 4

Author(s):

Guo-dong Sun ◽

You-ren Wang ◽

Can-fei Sun ◽

Qi Jin

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Signal Spectrum ◽

Planetary Motion ◽

Single Fault ◽

Planetary Gearbox ◽

Automatic Feature Extraction ◽

Mode Decomposition ◽

Intelligent Detection ◽

Diagnosis Method

Due to the existence of multiple rotating parts in the planetary gearbox—such as the sun gear, planet gears, planet carriers, and its unique planetary motion, etc.—the vibration signals generated under multiple fault conditions are time-varying and nonstable, thus making fault diagnosis difficult. In order to solve the problem of planetary gearbox composite fault diagnosis, an improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN) are proposed. The method takes as input the spectrum of the original vibration signal that contains rich information. First, the automatic feature extraction of signal spectrum is performed by I-CNN, while a classifier is used to diagnose the fault modes. Second, the composite fault signal is decomposed into multiple single fault signals by adaptive variational mode, and the signal is decomposed as a model input to diagnose the single fault component. Finally, a complete intelligent diagnosis of planetary gearboxes is conducted. Through experimental verification, the composite fault diagnosis method combining IPVMD and I-CNN will diagnose the composite fault and effectively diagnose the sub-fault included in the composite fault.

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Intelligent Fault Diagnosis Method Based on Operating Parameters in Nuclear Power Plant

Volume 1: Operations and Maintenance, Engineering, Modifications, Life Extension, Life Cycle and Balance of Plant; I&C, Digital Controls, and Influence of Human Factors ◽

10.1115/icone25-66494 ◽

2017 ◽

Author(s):

Zhiwu Ke ◽

Xu Hu ◽

Dawei Teng ◽

Mo Tao

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Real Time ◽

Bp Neural Network ◽

Nuclear Power ◽

Operating Parameters ◽

Feed Water ◽

Mechanical Equipment ◽

Single Fault ◽

Diagnosis Method

The safety of mechanical equipment is more important, it directly determines the safety of nuclear power plant operation, and even nuclear safety. So it is necessary to monitor the operating state of NPP system and mechanical equipment in real time by inspecting operating parameters. However, the key technology is real-time fault diagnosis of the mechanical equipment in NPP. Traditional fault diagnosis method based on analytic model is difficult to diagnose relevant and superimposed fault because of model error, disturbance and noise. This paper studies the application of fault diagnosis method based on BP neural network in NPP, and proposes an improved method for neural BP network method. For the feed-water system in the variable load operation process, we select the normal operation, the single feed-water valve fault, feed-water pump and feed-water valve superimposed fault as the analysis objects. One hundred points of data are extracted as BP algorithm training elements in these three processes averagely. The normal and abnormal conditions (including single fault and superimposed fault) can be accurately judged, but the single fault and superimposed failure would produce miscarriage of justice, about 2.4% of the single fault is diagnosed as superimposed fault, the diagnosis time delay is less than 1 second. These results meet the accuracy and real-time requirements. Then we study the application of support vector machine (SVM), which can make up for the deficiency of BP neural network. The results of this paper are useful for the real-time and reliable fault diagnosis of NPP.

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