Vehicles produce sound signals with varying temporal and spectral properties under different working conditions. These sounds are indicative of the condition of the engine. Fault diagnosis is a significantly difficult task in geographically remote places where expertise is scarce. Automated fault diagnosis can assist riders to assess the health condition of their vehicles. This paper presents a...

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to hav...

This paper presents efficient methods for online fault detection and diagnosis of Network-on-Chip (NoC) switches. The fault model considered in this research is a system level fault model based on the generic properties of NoC switch functionality. The proposed method is evaluated by fault simulation in a platform using this system level fault model. The experimental results show that with a re...

In accordance with intelligent complementary strategies, a new transformer fault diagnosis method is proposed based on rough set (RS) and fuzzy set (FS) and Bayesian optimal classifier in this paper. Through RS reduction, the diagnostic decision table is greatly simplified and fault symptoms information is compressed, dramatically, and the minimal decision rules can be obtained. In the light of...

A fault diagnosis and compensation problem for two-dimensional discrete time systems with time-varying state delays is studied in this paper. The concerned two-dimensional systems are described by the Fornasisi– Marchesini second model and are subject to unknown disturbances. First, a fault detection and diagnosis module is designed to obtain the information on sensor faults; a new fault detect...

This paper addresses the problem of diagnosis of flash ADC’s and proposes a fault diagnosis technique which employs the Differential NonLinearity (DNL) test data for fault location and identification of the analog components in the converter. In the flash ADC, a fault causes deviation of DNL data from the ideal one. Hence, DNL data can be considered as a functional signature of the ADC. This pr...

As the component parameters of analog circuits are influenced by tolerance, a soft fault-concept diagnosis model in this paper, which is based on the qualitative concepts of cloud model theory, is proposed to achieve the complete description of fault states and the fast diagnosis. The diagnosis model uses the cloud transform method to represent the conceptual soft classification of the multiple...