Baoping Cai

A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.

Bayesian Networks in Fault Diagnosis

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels

A novel safety integrity level (SIL) determination methodology based on multiphase dynamic Bayesian networks (MDBNs) for safety instrumented systems is proposed. Proof test interval phase and proof test phase are modeled separately using dynamic Bayesian networks and integrated together to form the MDBNs. The unified structure models of MDBNs for k-out-of-n architectures are constructed, and the procedures of automatic creation of conditional probability tables are developed. The target failure measures, that is, probability of failure on demand, average probability of failure on demand, probability of failing safely, average probability of failing safely, and SIL of safety instrumented systems operating in a low-demand mode, are evaluated using the proposed MDBNs. The effects of time interval of MDBNs, common cause weight, imperfect proof test, and repair on model precision are researched. User-friendly SIL determination software is developed by using MATLAB GUI to assist engineers in determining the SIL value.

Availability-based engineering resilience metric and its corresponding evaluation methodology

Several resilience metrics have been proposed for engineering systems (e.g., mechanical engineering, civil engineering, critical infrastructure, etc.); however, they are different from one another. Their difference is determined by the performances of the objects of evaluation. This study proposes a new availability-based engineering resilience metric from the perspective of reliability engineering. Resilience is considered an intrinsic ability and an inherent attribute of an engineering system. Engineering system structure and maintenance resources are principal factors that affect resilience, which are integrated into the engineering resilience metric. A corresponding dynamic-Bayesian-network-based evaluation methodology is developed on the basis of the proposed resilience metric. The resilience value of an engineering system can be predicted using the proposed methodology, which provides implementation guidance for engineering planning, design, operation, construction, and management. Some examples for common systems (i.e., series, parallel, and voting systems) and an actual application example (i.e., a nine-bus power grid system) are used to demonstrate the application of the proposed resilience metric and its corresponding evaluation methodology.

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

Risk analysis of atmospheric and vacuum distillation unit using Bayesian networks

The accidents occurred in chemical plants often regard as low frequency and high consequence. It is necessary to raise the risk analysis for the petrochemical system to help people to find the weakest process in the whole system thus people can strength the process to improve the safety. In this paper, a methodology by using Bayesian Networks (BNs) to give a model for a chemical plant has been raised. According to the harm extend, the methodology classifies the events into three layers, cause, incident, and accident. Then the application of the methodology is illustrated by analyzing an atmospheric and vacuum distillation unit. The model identifies the most possible cause when an accident happened. After that, mutual information and variety of beliefs are calculated in order to find the most sensitive event of an accident. The study gives suggestions to people of identification the most relevant and weakest point in the plant.

A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

The word resilience originally originates from the Latin word “resiliere”, which means to “bounce back”. The concept has been used in various fields, such as ecology, economics, psychology, and society, with different definitions. In the field of critical infrastructure, although some resilience metrics are proposed, they are totally different from each other, which are determined by the performances of the objects of evaluation. Here we bridge the gap by developing a universal critical infrastructure resilience metric from the perspective of reliability engineering. A dynamic Bayesian networks-based assessment approach is proposed to calculate the resilience value. A series, parallel and voting system is used to demonstrate the application of the developed resilience metric and assessment approach.The word resilience originally originates from the Latin word “resiliere”, which means to “bounce back”. The concept has been used in various fields, such as ecology, economics, psychology, and society, with different definitions. In the field of critical infrastructure, although some resilience metrics are proposed, they are totally different from each other, which are determined by the performances of the objects of evaluation. Here we bridge the gap by developing a universal critical infrastructure resilience metric from the perspective of reliability engineering. A dynamic Bayesian networks-based assessment approach is proposed to calculate the resilience value. A series, parallel and voting system is used to demonstrate the application of the developed resilience metric and assessment approach.