Anomaly Detection and Fault Diagnosis

2019 ◽  
pp. 331-366
Author(s):  
Yu Ding
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection

A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays

2021 ◽  
Vol 234 ◽  
pp. 113950
Author(s):  
Chenxi Li ◽  
Yongheng Yang ◽  
Kanjian Zhang ◽  
Chenglong Zhu ◽  
Haikun Wei
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Diagnosis Technique

Growing Structure Multiple Model Systems for Anomaly Detection and Fault Diagnosis

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Jianbo Liu ◽  
Dragan Djurdjanovic ◽  
Kenneth Marko ◽  
Jun Ni
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
System Development ◽  
Simulation Software ◽  
Model Systems ◽  
Multiple Model ◽  
Detection Scheme ◽  
Engine Simulation ◽  
Local Decision ◽  
Residual Errors

A new anomaly detection scheme based on growing structure multiple model system (GSMMS) is proposed in this paper to detect and quantify the effects of anomalies. The GSMMS algorithm combines the advantages of growing self-organizing networks with efficient local model parameter estimation into an integrated framework for modeling and identification of general nonlinear dynamic systems. The identified model then serves as a foundation for building an effective anomaly detection and fault diagnosis system. By utilizing the information about system operation region provided by the GSMMS, the residual errors can be analyzed locally within each operation region. This local decision making scheme can accommodate for unequally distributed residual errors across different operational regions. The performance of the newly proposed method is evaluated through anomaly detection and quantification in an electronically controlled throttle system, which is simulated using a high-fidelity engine simulation software package provided by a major automotive manufacturer for control system development.

Machine Learning for Diagnosis of Event Synchronization Faults in Discrete Manufacturing Systems

2021 ◽  
pp. 1-31
Author(s):  
Joseph Cohen ◽  
Baoyang Jiang ◽  
Jun Ni
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Anomaly Detection ◽  
Unsupervised Learning ◽  
Learning Algorithms ◽  
Discrete Event ◽  
Machine Learning Algorithms ◽  
Programmable Logic ◽  
Discrete Manufacturing

Abstract Common in discrete manufacturing, timed event systems often have strict synchronization requirements for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based random forest and extreme random forest classifiers achieve excellent performance with a precision and recall score of 0.96 for multi-fault classification. Additionally, the unsupervised learning approach yields anomaly detection rates of 98% with false alarms under 3% with a training set 99% smaller than the supervised learning classifiers. These results obtained on a real use case are promising to enable prognostic tools in industrial automation systems in the future

scholarly journals A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1221
Author(s):  
Xinwei Cong ◽  
Caiping Zhang ◽  
Jiuchun Jiang ◽  
Weige Zhang ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Dominant Role ◽  
Lithium Ion ◽  
Window Size ◽  
Operational Reliability ◽  
Signal Features ◽  
Diagnosis Method

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Measurement ◽  
2014 ◽  
Vol 55 ◽  
pp. 343-352 ◽  
Author(s):  
Afrooz Purarjomandlangrudi ◽  
Amir Hossein Ghapanchi ◽  
Mohammad Esmalifalak
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Detection Algorithm ◽  
Data Mining Approach

scholarly journals Fault-Diagnosing SLAM for Varying Scale Change Detection

2019 ◽  
Author(s):  
sugimoto takuma ◽  
Yamaguchi Kousuke ◽  
kanji tanaka
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Change Detection ◽  
Image Representation ◽  
Bag Of Words ◽  
Visual Slam ◽  
Image Comparison ◽  
Significant Challenge ◽  
Scale Change ◽  
Recent Advances

In this paper, we present a new fault diagnosis (FD) -based approach for detection of imagery changes that can detect significant changes as inconsistencies between different sub-modules (e.g., self-localization) of visual SLAM. Unlike classical change detection approaches such as pairwise image comparison (PC) and anomaly detection (AD), neither the memorization of each map image nor the maintenance of up-to-date place-specific anomaly detectors are required in this FD approach. A significant challenge that is encountered when incorporating different SLAM sub-modules into FD involves dealing with the varying scales of objects that have changed (e.g., the appearance of small dangerous obstacles on the floor). To address this issue, we reconsider the bag-of-words (BoW) image representation, by exploiting its recent advances in terms of self-localization and change detection. As a key advantage, BoW image representation can be reorganized into any different scaling by simply cropping the original BoW image. Furthermore, we propose to combine different self-localization modules with strong and weak BoW features with different discriminativity, and to treat inconsistency between strong and weak self-localization as an indicator of change. The efficacy of the proposed approach of FD with/without combining AD and/or PC was experimentally validated.

scholarly journals A Novel Vehicle Gearbox Fault Diagnosis Approach Based on Collective Anomaly Detection

2021 ◽  
Vol 252 ◽  
pp. 01052
Author(s):  
Zhongfeng Hu ◽  
Xiaodi Huang
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Data Distribution ◽  
Likelihood Estimate ◽  
Iteration Algorithm ◽  
Multivariate Gaussian Distribution ◽  
Test Dataset ◽  
Sample Data ◽  
Using Data ◽  
Collective Anomaly

Targeting the problem of gearbox fault diagnosis, we proposed a novel semi-supervised approach based on collective anomaly detection. Based on the limited sample data, the principle of the approach is to detect whether a test dataset contains abnormal patterns by using data distribution as the metric. The sequence obeying unexpected distribution will be identified as collective anomaly, which may be generated by fault patterns. The approach consists of three steps. First, the mixture of multivariate Gaussian distribution is used to fit the structure of sample dataset and test dataset. Then, based on maximum likelihood estimate algorithm, we hope to search the optimal parameters which can fit the data distribution with the highest degree. Finally, the fixed point iteration algorithm is used to solve likelihood estimate functions. Experimental results demonstrate that the proposed approach can be used to find fault patterns of gearbox without the prior knowledge of their generated mechanisms.

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