Automotive signal fault diagnostics. I. Signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection

Crossman, Jacob; Guo, Hong; Murphey, Yi Lu; Cardillo, Juan

doi:10.1109/tvt.2002.807635

Cited by 68 publications

(24 citation statements)

References 15 publications

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“…The faults are normal state (N), ball fault (B), inner race fault (I), outer race defects at 3, 6, and 12 o'clock positions (O3, O6, and O12). Larger Mahalanobis distance in the table represents the higher level of linear separability for two different groups [19]. Comparing N with B (N in fault class 1 and B in fault class 2 or N in fault class 2 and B in fault class 1), both values of the CMSE and MSE are high and the value of the CMSE is higher than that of MSE, indicating that the normal state can easily be distinguished from ball fault and the CMSE has the higher distinguishability.…”

Section: Performance Assessmentmentioning

confidence: 99%

Time Series Analysis Using Composite Multiscale Entropy

Lin

et al. 2013

Entropy

281

204

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Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. Simulation results on both white noise and 1/f noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.

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Section: Performance Assessmentmentioning

confidence: 99%

Time Series Analysis Using Composite Multiscale Entropy

Lin

et al. 2013

Entropy

281

204

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“…Vehicle telemetry mining in the automotive domain has been applied in various domains, including safety improvement, fault detection, and efficiency gains (Crossman, Guo, Murphey, & Cardillo, 2003;Murphey, Crossman, Chen, & Cardillo, 2003;Murphey et al, 2008;Kruse, Steinbrecher, & Moewes, 2010;X. Huang, Tan, & He, 2011).…”

Section: Data Mining Of the Dmdmentioning

confidence: 99%

Investigating the Feasibility of Vehicle Telemetry Data as a Means of Predicting Driver Workload

Taylor

Griffiths

Bhalerao

et al. 2017

International Journal of Mobile Human Computer Interaction

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Driving is a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to use vehicle telemetry data as a performance measure for workload. In this paper, we present the Warwick-JLR Driver Monitoring Dataset (DMD) and analyse it to investigate the feasibility of using vehicle telemetry data for determining the driver workload. We perform a statistical analysis of subjective ratings, physiological data, and vehicle telemetry data collected during a track study. A data mining methodology is then presented to build predictive models using this data, for the driver workload monitoring problem

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“…Data mining of CAN-bus data has been used in several applications, including fault detection (Crossman et al, 2003;Guo et al, 2000), driver monitoring (Mehler et al, 2012;Taylor et al, 2013b), and driving conditions monitoring, which is surveyed by Wang and Lukic (2011) and is the focus of this paper. Fault detection aims to determine whether there is a vehicle failure and what may have caused the it.…”

Section: Related Workmentioning

confidence: 99%

“…In fault detection, both Guo et al (2000) and Crossman et al (2003) successfully apply wavelet analysis to split telemetry signals into segments, from which several features are extracted. The extracted features include the segment length, minimum and maximum values, as well as averages and fluctuations.…”

Section: Related Workmentioning

confidence: 99%

Data Mining for Vehicle Telemetry

Taylor

Griffiths

Bhalerao

et al. 2016

Applied Artificial Intelligence

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This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.

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Automotive signal fault diagnostics. I. Signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection

Cited by 68 publications

References 15 publications

Time Series Analysis Using Composite Multiscale Entropy

Time Series Analysis Using Composite Multiscale Entropy

Investigating the Feasibility of Vehicle Telemetry Data as a Means of Predicting Driver Workload

Data Mining for Vehicle Telemetry

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