Search

Tool condition monitoring (TCM) is crucial to ensure good quality products and avoid downtime. Machine learning has proven to be vital for TCM. However, existing works are predominately based on supervised learning, which hinders their applicability in real-world manufacturing settings, where data labeling is cumbersome and costly with in-service machines. Additionally, the existing unsupervised solutions mostly handle binary decision-based TCM which is unable to fully reflect the dynamics of tool wear progression. To address these issues, we propose different unsupervised and semisupervised five-class tool wear recognition frameworks to handle fully unlabeled and partially labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. For the semisupervised frameworks, we considered designs where labeled data influence only feature learning, classifier building, or both. We also investigated different training configurations of SSAE regarding the supervision level. We applied the frameworks on two run-to-failure datasets of milling tools, recorded using a microphone and an accelerometer. Single sensor and multisensor data under different percentages of labeled training data were considered in the evaluation. The results showed which of the frameworks led to the best predictive performance under which data settings, and highlighted the significance of sensor fusion and discriminative feature representations in combating the unavailability and scarcity of labels, among other findings. The highest macro-F1 achieved for the two datasets with fully unlabeled data reached 87.52% and 75.80%, respectively, and over 90% when only 25% of the training observations were labeled.

A fault diagnosis model for rolling bearing based on complementary ensemble adaptive local iterative filtering (CEALIF), Laplacian score (LS) feature selection, and genetic algorithm-based backpropagation neural network (GA-BPNN) is proposed in this article. When the rolling bearing fails, the field-measured waveforms usually shows strong nonlinearity and non-stationary characteristics. Adaptive Local Iterative Filtering (ALIF) is an alternative novel approach to empirical mode decomposition to decompose complex signals into multiple intrinsic mode functions (IMFs), but modal aliasing will occur in actual processing. Aiming at the phenomenon of modal aliasing, a noise-assisted analysis methodology, namely complementary ensemble adaptive local iterative filtering, which could overcome the modal aliasing problem of ALIF. This article applies it to the pre-processing of rolling bearing time series. Then, the time domain (TD) statistical features of the IMFs, their Fourier frequency domain (FD) features, and the time frequency domain (TFD) energy features are extracted to capture the fault information. Meanwhile, to avoid feature redundancy and enhance the diagnostic performance, the LS is adopted to rank the features to improve the fault characteristics. Subsequently, the optimized feature vectors are entered into the GA-BPNN to automatically achieve the fault type recognition. The experimental data analysis results of rolling bearings indicate that the model can effectively diagnose the degree and type of failure.

The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes. [ABSTRACT FROM AUTHOR]

A novel rolling bearing fault diagnosis method based on hierarchical fuzzy entropy (HFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT) is proposed in this paper. Focus on the difficulty of extracting fault feature from the non-linear and non-stationary vibration signal under complex operating conditions, HFE method is utilized for fault feature extraction. Compared with multi-scale fuzzy entropy (MFE) method, HFE method considers both the low and high frequency components of the vibration signals, which can provide a much more accurate estimation of entropy. Besides, Laplacian score (LS) method is introduced to refine the fault feature by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results demonstrate that the proposed method is effective in recognizing the different categories and severities of rolling bearings faults. [ABSTRACT FROM AUTHOR]

A new bearing vibration feature extraction method based on multiscale permutation entropy (MPE) and improved support vector machine based binary tree (ISVM-BT) is put forward in this paper. Local mean decomposition (LMD), a new self-adaptive time–frequency analysis method, is utilized to decompose the roller bearing vibration signal into a set of product functions (PFs) and then MPE method is used to characterize the complexity of the principal PF component in different scales. After the feature extraction, a new pattern recognition approach called ISVM-BT is introduced to accomplish the fault identification automatically, which has the priority of high recognition accuracy compared with other classifiers. Besides, the Laplacian score (LS) is introduced to refine the fault feature by sorting the scale factors. Finally, the rolling bearing fault diagnosis method based on LMD, MPE, LS and ISVM-BT is proposed and the experimental results indicate the proposed method is effective in identifying the different categories of rolling bearings. [ABSTRACT FROM AUTHOR]