A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications: An International Journal
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Applied Soft Computing
Application of an intelligent classification method to mechanical fault diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of mother wavelet functions for automatic gear and bearing fault diagnosis
Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
Fault diagnosis of ball bearings using continuous wavelet transform
Applied Soft Computing
A knowledge driven approach to aerospace condition monitoring
Knowledge-Based Systems
A case-based knowledge system for safety evaluation decision making of thermal power plants
Knowledge-Based Systems
Forecasting tourism demand based on empirical mode decomposition and neural network
Knowledge-Based Systems
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
Information Sciences: an International Journal
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To better equip with a non-expert to carry out the diagnosis operations, a new method for intelligent fault identification of rotating machinery based on the empirical mode decomposition (EMD), dimensionless parameters, fault decision table (FDT), MLEM2 rule induction algorithm and improved rule matching strategy (IRMS) is proposed in this paper. EMD is used to preprocess the vibration signals for mining the fault characteristic information more accurately. Then, dimensionless parameters are extracted from both the decomposed signals in time domain and envelop spectrum in frequency domain respectively to form the conditional attributes of a FDT. Moreover, MLEM2 algorithm is run directly on the FDT to generate decision rules imbedded in the data. To make the following classification process more robust, the IRMS is adopted to resolve the conflicting and non-matching problems. Finally, data of rolling element bearings with four typical working conditions is used to evaluate the performance of the proposed method. The testing result demonstrates that the method has high accuracy and systematically good performance. It is proved to be a convenient, concise, interpretable and reliable way to diagnose bearings' faults. The advantages are also confirmed by the comparisons with the other two approaches, i.e. the principal component analysis (PCA) and probabilistic neural network (PNN) based method as well as the wavelet transform (WT) and genetic algorithm (GA) based one. Furthermore, thank to the FDT working as a data interface, the method is more transplantable, therefore it may be applied to diagnose other types of rotating machines effectively.