A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses

Xiaoxia Liang, Fang Duan, Ian Bennett and David Mba
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Xiaoxia Liang: School of Engineering, London South Bank University, London SE1 0AA, UK
Fang Duan: School of Engineering, London South Bank University, London SE1 0AA, UK
Ian Bennett: Technology Manager Services, Shell Research Ltd., Floor 21, Shell Centre, London SE1 7NA, UK
David Mba: Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK

Energies, 2020, vol. 14, issue 1, 1-25

Abstract: Large rotating machinery, such as centrifugal gas compressors and pumps, have been widely applied and acted as crucial components in the oil and gas industries. Breakdowns or deteriorated performance of these rotating machines can bring significant economic loss to the companies. In order to conduct effective maintenance and avoid unplanned downtime, a system-wide health indicator is proposed in this paper. The health indicator not only uses a dynamic risk profile, but also considers financial loss and the fault probability based on condition monitoring data. This methodology is carried out by four steps: fault detection, probability of fault calculation, consequence of fault calculation and dynamic risk assessment. In our methodology, the fault probability is calculated by robust Mahalanobis distance, presenting as a system-wide feature from a sparse autoencoder fault detection model enabled early fault detection. The value of the health indicator is presented in financial loss, which assists in effective operational decision-making in a process system. To evaluate the performance of the proposed indicator, two case studies were carried out—one case tested on multivariate industrial data obtained from a pump, and another one tested on an industrial data set from a compressor. Results prove that the integrated health indicator can detect the faults at their incipient stages, indicate the degradation of the system with dynamically updated process risk at each sampling instant, and suggest an appropriate shutdown time before the system suffers severe damage. In addition, this methodology can be adapted to other machines’ health assessments, such as those of turbines and motors. The presented method of processing the industrial data set can benefit relevant readers.

Keywords: condition monitoring; health indicator; dynamic risk assessment; financial loss; fault probability; decision making (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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