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.
Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis.
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.
Main contribution of the paper is to propose nonlinear fault diagnosis methods based on multiscale contribution plots.
In this final part, we discuss fault diagnosis methods that are based on historic process knowledge.
Since the faults of VOBEs in HSRs are usually uncertain and complex, the current fault diagnosis methods are mainly based on manual judgement in real-world operations, which is generally inefficient and insecurity with the big rail traffic data.
To view data-driven methods as an integrated type, we can re-divide fault diagnosis methods into three subclasses, namely analytical model-based methods, qualitative knowledge-based methods, and data-driven based methods (DDBM), where DDBM can be further divided into data transform based methods (DTBM) and data reasoning based methods (DRBM).
Numerous fault diagnosis methods for induction motors have been proposed so far which can be classified in three main types depending on their diagnosis procedure [1], namely model based, signal based, and data based.
However, existing fault diagnosis methods of planetary gearboxes are hard to realize fault diagnosis using incomplete diagnostic information that simultaneously contains two categories of unknown attribute values.
