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By introducing the class information into the traditional non-negative matrix factorization (NMF), an improved NMF algorithm named as discriminative NMF (DNMF) was developed and a novel imaged based fault diagnosis method was proposed by the combination of the DNMF and the KNN classifier.
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In the past several decades, the image based fault diagnosis methods have provided efficient ways for the diesel engine fault diagnosis.
The rule-based fault diagnosis method presented is efficient to isolate multiple faults of air handling units.
The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.
Firstly, an observer-based fault diagnosis method is proposed to estimate the marking with unknown initial markings and meanwhile, to diagnose the faulty behavior.
Furthermore, the rule-based fault diagnosis method is extended to isolate simultaneous multiple faults of VAV air handling units in the mechanical cooling operation mode.
In this paper, a robust fault detection and diagnosis strategy is presented for multiple faults of air handling units by using the residual-based exponentially weighted moving average (EWMA) control chart method and the rule-based fault diagnosis method.
From a new perspective to further investigate Venkatasubramanian's classification, data-driven based fault diagnosis not only includes a large part of techniques in process history based method, but also some belonging to qualitative model-based methods.
Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis.
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox.
To solve the problem of early fault diagnosis of rolling bearing under strong background noise, a fault diagnosis method based on integration of Resonance-based Sparse Signal Decomposition (RSSD) and Wavelet Transform (WT) is proposed in this paper.
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