Sequential Fault Diagnosis Using an Inertial Velocity Differential Evolution Algorithm

• Published in: International journal of automation and computing Vol. 16; no. 3; pp. 389 - 397
• Main Authors: Qiu, Xiao-Hong, Hu, Yu-Ting, Li, Bo
• Format: Journal Article
• Language: English
• Published: Beijing Institute of Automation, Chinese Academy of Sciences 01.06.2019; Springer Nature B.V
• Subjects: Adaptive algorithms; Adaptive systems; Algorithms; Automation; CAE) and Design; Computer Applications; Computer simulation; Computer-Aided Engineering (CAD; Control; Decision trees; Diagnostic systems; Dynamic programming; Electronic systems; Engineering; Evolutionary algorithms; Evolutionary computation; Fault diagnosis; Fitness; Genetic algorithms; Heuristic; Mechatronics; Optimization; Particle swarm optimization; Research Article; Robotics; Test sets; Velocity; evolutionary computation; fault diagnosis; testability; fault isolation rate (FIR); Differential evolution (DE)
• ISSN: 1476-8186; 2153-182X; 1751-8520; 2153-1838
• DOI: 10.1007/s11633-016-1008-0

The optimal test sequence design for fault diagnosis is a challenging NP-complete problem. An improved differential evolution (DE) algorithm with additional inertial velocity term called inertial velocity differential evolution (IVDE) is proposed to solve the optimal test sequence problem (OTP) in complicated electronic system. The proposed IVDE algorithm is constructed based on adaptive differential evolution algorithm. And it is used to optimize the test sequence sets with a new individual fitness function including the index of fault isolation rate (FIR) satisfied and generate diagnostic decision tree to decrease the test sets and the test cost. The simulation results show that IVDE algorithm can cut down the test cost with the satisfied FIR. Compared with the other algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA), IVDE can get better solution to OTP.