Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA. (28th February 2018)

• Record Type: Journal Article
• Authors: He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong
• Is Part Of: Measurement science & technology. Volume 29:Number 4(2018:Apr.)
• Publication Date: 2018-02-28
• Subjects: fault diagnosis -- analog circuit -- cross wavelet transform (XWT) -- bilateral 2D linear discriminant analysis (B2DLDA) -- vector-valued regularized Kernel function approximation (VVRKFA); Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aaa33a
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the fault signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the fault. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit faults classification have demonstrated that the proposed diagnosis scheme has an advantage over other approaches.