Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors

Na, Wonbin; Park, Changwoo; Lee, Seokjoo; Yu, Seongo; Lee, Hyeongcheol

doi:10.3390/s18082720

Cited by 13 publications

(11 citation statements)

References 44 publications

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“…In recent years, Outlier Detection has been increasingly applied in sensor data processing [1][2][3][4][5][6][7][8][9]. Despite the countless contributions made over the years, there is continuing research on the subject, mainly because there has been an increase in computational power.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions but must be computed numerically by means of Monte Carlo. This paper provides the first results on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection but the lower the rates of outlier identification. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.

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Section: Introductionmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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Section: Introductionmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Rofatto¹,

Matsuoka²,

Klein³

et al. 2020

Preprint

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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of the modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions, but must be computed numerically by means of Monte Carlo. This paper provides the first results about Monte Carlo-based critical value inserted to different scenarios of correlation between the outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, overidentifications and statistical overlap associated with IDS in the presence of a single outlier. Based on such probability levels we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for the case where IDS is in play. MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances that the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection, but the lower the rates of outlier identification. For that case, the larger the Type I Error, the larger the ratio between MIB and MDB. We also highlight that an outlier becomes identifiable when the contribution of the measures to the wrong exclusion rate decline simultaneously. In that case, we verify that the effect of the correlation between the outlier statistics on the wrong exclusion rates becomes insignificant from a certain outlier magnitude, which increases the probability of identification.

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“…The first-class includes the detection of single faults by analyzing one or multiple parameters; the second class covers the detection of different faults with multiple parameters and processing techniques, and the last one contains the mixed techniques of various computing-intensive approaches to analyze different electrical and mechanical parameters in order to detect multiple faults [61][62][63][64]. In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts were made for the application of intelligent algorithms [66,67] including new approaches to Fault Detection and Isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69][70][71][72][73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors

Cited by 13 publications

References 44 publications

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

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