Size and Location Diagnosis of Rolling Bearing Faults: An Approach of Kernel Principal Component Analysis and Deep Belief Network

Abstract

Diagnosing incipient faults of rotating machines is very important for reducing economic losses and avoiding accidents caused by faults. However, diagnoses of locations and sizes of incipient faults are very difficult in a noisy background. In this paper, we propose a fault diagnosis method that combines kernel principal component analysis (KPCA) and deep belief network (DBN) to detect sizes and locations of incipient faults on rolling bearings. Effective information of raw vibration signals processed by KPCA method is used as input signals of the DBN of which weights of the first RBM are initialized by contribution rates of principal components. A DBN with complex structures can be cut into a briefer network by KPCA-DBN model. That model reduces network structure and increases convergence rate. As a result, an average test accuracy by KPCA-DBN can reach 99.1% for identification of 12 labels including incipient faults and the training time is 28s which is half of that by DBN model. The average accuracy of rolling bearing location detection nearly gets to 100% and the average accuracy of fault size detection is above 99%. Compared with SVM, BP, CNN, Deep EMD-PCA, CNN-SVM and DBN, it is found that training time can be shortened and detection accuracy can be improved by KPCA-DBN model. The proposed method is beneficial to realize sizes and locations detection of incipient faults online.

A feature reconstruction and SAE model based diagnosis method for multiple mixed faults

“…Therefore, even in the case of an imbalanced distribution of condition data, the proposed method can quickly and accurately identify fault types and determine the health modes for the gearbox. MDRMA-MSCM [17] 97.714 97.428 MA-MOCO [18] 98.285 98.143 Improved deep forest [30] 93.238 92.976 MP-DBN [31] 97.524 97.128 KPCA + AE [32] 96.436 96.064 CNN + TL [33] 97.143 96.857 The proposed method 99.786 99.405…”

“…In this section, six methods multi-dilation rates and multi-attention mechanism (MDRMA-MSCM) [17], MA-MOCO [18], improved deep forest [30], mixed pooling deep belief network (MP-DBN) [31], AE [32], and convolutional neural network + transfer learning (CNN + TL [33]) were employed to analyze the same dataset. Considering the testing stability, ten consecutive experiments were conducted; the average accuracy of the comparison methods is shown in table 4.…”

“…The MA-MOCO learning-based model designed in [18] successfully handled wind turbine fault diagnosis, which is difficult to generalize for other mechanical condition recognition. As described in [30], the improved deep forest-based method reduces the burden of hyperparameter tuning. However, it ignores uneven data distribution, and there is still room for improvement in diagnostic accuracy.…”

“…Especially, to demonstrate further the classification and generalization ability of the developed scheme, six methods (MDRMA-MSCM [17], MA-MOCO [18], improved deep forest [30], MP-DBN [31], AE [32], and CNN + TL [33]) tested in section 5.1 are adopted again to analyze the same dataset of rolling bearings. Neglecting potential randomness and contingency, ten consecutive trials were developed; the final statistical results of the comparison methods are listed in table 11.…”

Fault diagnosis of coal mills based on a dynamic model and deep belief network

“…Deep belief network (DBN), a branch of deep learning, is widely used in fault diagnosis by virtue of its multi-type classification capability [18]. Mushtaq et al [19] and Tang et al [20] reviewed the deep learning methods employed to diagnose the faults of rotating machinery and affirmed the effectiveness of DBN in this field.…”