This paper presents some results that were obtained in fault diagnosis for a wood-drying process. It is diffcult to detect and diagnose faults or failure in the drying process due to complex dynamic nonlinearity, coupling effects among key variables, and process disturbances caused by the variation of lumber sizes, species and environmental factors. In this paper, a real-time fault diagnosis al...

In order to process the vagueness in vibration fault diagnosis of rolling bearing, a new correlation coefficient of simplified neutrosophic sets (SNSs) is proposed. Vibration signals of rolling bearings are acquired by an acceleration sensor, and a morphological filter is used to reduce the noise effect. Wavelet packet is applied to decompose the vibration signals into eight subfrequency bands,...

Fault diagnosis of pneumatic system of automatic production line is studied in this paper. A fuzzy neural network fault diagnosis expert system and pneumatic circuit fault diagnosis instrument are designed. The experiment device is built as well. In the end some experiments are done, whose results show that the expert system using fuzzy neural network can diagnose fast and truly fault of pneuma...

To effectively diagnose the deterministic faults of a LPRE ground-testing bed, the fault diagnosis method based on PCA and SOM is proposed. The dimension reduction process of PCA not only reduces data size, but also reduces noise influence. It also implements a visualization of fault status identification and fault variable orientation by SOM. Simulation and real fault data results indicate tha...

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal an...

In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet co...

In order to diagnose incipient fault of analog circuits effectively, an analog circuit incipient fault approach by using kernel entropy component analysis (KECA) as a preprocessor is proposed in the paper. Time responses are acquired by sampling outputs of the circuits under test. Raw features with high dimension are generated by wavelet transform. Furthermore, lower dimensional features are pr...

This paper discusses the fault tolerant control problem for discrete event systems modeled by Petri nets. The fault is represented by the unobservable transitions. Firstly, an observer-based fault diagnosis method is proposed to estimate the marking with unknown initial markings and meanwhile, to diagnose the faulty behavior. Then, an adaptive fault tolerant controller is designed to maintain t...

When a fault occurs in a system, the main problem to be addressed is to raise an alarm, ideally diagnose what fault has occurred, and then decide how to deal with it. The problem of detecting a fault, finding the source/location and then taking appropriate action is the basis of fault tolerant control. In this chapter, an introduction to fault tolerant control (FTC) and fault detection and isol...

In order to diagnose the occurrence of a fault event, it is first necessary to verify if the language of the system is diagnosable with respect to an observable event set and a fault event set. This verification can be carried out, in polynomial time, by using a verifier automaton. In some cases, the language of the system remains diagnosable even if some events of the observable event set beco...