Abstract
As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. Compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery.
Highlights
Rotating machinery is the most popular type of equipment used in mechanical engineering industrial applications
The experimental results show that, compare with dimensionless method, the accuracy of bearing fault diagnosis can be increased by 9.22% at most by mutual dimensionless processing
To solve the above problems, this paper proposes the application of semi supervised fault diagnosis method based on simulated annealing and genetic algorithm optimization in bearing fault diagnosis
Summary
Rotating machinery is the most popular type of equipment used in mechanical engineering industrial applications. Rolling bearings are one of the most important components of rotating machinery. The rapid developments in science and technology have increased the complexity of rotating machinery structures, increasing the probability of rolling bearing failure [1]. Diagnosis for predicting rolling bearing failures is of particular significance [2], [3]. A common fault diagnosis method is to analyze vibration signals that contain mechanical fault information [4], [5]. This method comprises two important steps: signal feature extraction and fault status identification. An effective way to identify failure modes is to use fuzzy c-means (FCM) clustering based on the fuzzy theory [6], [7]
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