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With respect to the subjective and nonlinear factors inherent in the importance identification of a fault tree analysis (FTA), a new importance measure of FTA is proposed based upon possibilistic information entropy. After investigating the possibilistic information semantics, measure-theoretic terms and entropy-like models, a two-dimensional framework is constructed by combining both the set theory...
Owing to human's experiences being rarely applied in the traditional fault diagnosis procedure, a kind of fault diagnosis model of rotating speed sway is established based on Fuzzy Neural Network (FNN) for avion engine. The fault diagnosis model can simulate expert's fault diagnosis procedure, and improve fault diagnosis performance of rotating speed sway for avion engine. The simulation results show...
Aiming at the online fault diagnoses, the texture features which are usually used in image processing are firstly applied in the early fault signal recognition problems. After the parameter R based on gray-level co-occurrence matrix is defined, the parameter R extraction method of texture features is presented. Then, the novel fault signal recognition algorithm based on the parameter R of the texture...
A novel method for detection and diagnosis the bearing inner and outer race fault according to bi-spectrum analysis technique is presented. The bi-spectrum analysis is widely recognized as an effective technique for machinery fault diagnosis using vibration signals since it can be used to eliminate the effect of strong noise. This advantage makes it very suitable for extracting useful features from...
A novel method for detection and diagnosis of the gear wear fault according to complex Morlet wavelet amplitude and phase map is presented. The continuous wavelet transform is widely recognized as an effective technique for machinery fault diagnosis using vibration signals since it can be used to detect both stationary and transitory signals. This advantage makes it very suitable for the detection...
By analyzing the complexity of fault diagnosis grid, we researched the bi-particle projection mode of fault diagnosis grid composed by project nodes and service resource nodes. As the effacter in grid, every project node and service resource node would adopt different strategies, such as selection, decision-making and competition, which will result in self-evolvement and evolvement of whole grid....
To overcome the sensor system problem of fault diagnosis and signal recovery for autonomous underwater vehicle (AUV), a method based on strong tracking filter (STF) theory and singer model of first order time correlation function was proposed. The STF-Singer model by combining the signal processing method and STF method dose not need the accurate mathematical model of the controlled plant, and it...
Transformers fault diagnosis plays a vital role in running security and reliability. The detected information is collected from the disperse sensor, which is lack of the data fusion analysis and easily lead to decision error and leak. A model of oil-immersed transformer fault diagnosis based on the collaborative method of Kernel C-Means Clustering (KCM) and multi-source information data fusion is...
In order to improve the fault diagnosis accuracy for fan of coal to establish the case base of fans fault and diagnosis parameter, based on CBR, the case reasoning method to make the diagnosis for the fans. Case searching, matching and reusing used by the static confidence thresholds and dynamic confidence thresholds comprehensive judgment method; improve the applicability of the fault diagnosis system.
Since reciprocating compressor (RC) is a key facility for many industries, the study of its fault diagnosis is thus particularly important. This paper proposes a new method for predicting the fault degree of RC by using a manifold learning method. The main idea of the proposed method can be summarized as follows: first, employ a manifold learning algorithm to directly deal with RC's cylinder pressure...
In this paper, a novel approach based on rough set theory and a pair wise comparison table for reduction and ordering of basic events in fault tree is proposed. The details of the approach, together with the basic concepts of rough set theory, are presented. A case study is used to illustrate the application of the proposed approach. Results show that a reasonable ordering of basic events in a fault...
A novel method for detection and diagnosis the bearing fault according to instantaneous energy spectrum (IES) based on empirical mode decomposition (EMD) method is presented. EMD can adaptively decompose the vibration signal into a series of zero mean oscillatory functions called intrinsic mode functions (IMFs). Hilbert transform can track the instantaneous amplitude and instantaneous frequency of...
With the increasing demand of stable running condition of mechanical products and low maintenance costs of machinery devices, fault detection and diagnosis attracted considerable interests, early fault diagnosis is desirable for accuracy and appropriate assessment, due to the fact that it could provide fault information as soon as possible and prevent fast deteriorating of the failure. In this paper,...
In order to solve the problem of feature extraction in the gear fault pattern recognition, a method of feature extraction based on atomic decomposition was proposed. Signals are rapidly decomposed using matching pursuit with the constructed Gabor dictionary. The frequency parameters and respective correlation values of the selected atoms constitute the feature vector of signal. Binary Tree Support...
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