We develop a novel deep learning framework to achieve highly-accurate machine fault diagnosis using transfer learning to enable and accelerate the training of deep neural network. Compared with existing methods, the proposed method is faster to train and more accurate. First, original sensor data are converted to images by conducting a Wavelet transformation to obtain time-frequency distributions. Next, a pre-trained network is used to extract lower level features. The labeled time-frequency images are then used to fine-tune the higher-levels of the neural network architecture. This paper creates a machine fault diagnosis pipeline and experiments are carried out to verify the effectiveness and generalization of the pipeline on three main mechanical datasets including induction motors, gearboxes, and bearings with sizes of 6,000, 9,000, and 5,000 time series samples, respectively. We achieve state-of-the-art results on each dataset, with most datasets showing test accuracy near 100%, and in the gearbox dataset, we achieve significant improvement from 94.8% to 99.64%. We created a repository including these datasets located at mlmechanics.ics.uci.edu.

Deep learning architecture, which exploits multiple hidden layers to learn hierarchical representations automatically from massive input data, presents a promising tool for characterizing fault conditions. This paper proposes a deep learning-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of convolutional neural network (CNN) in images. The proposed deep model is able to learn from multiple types of sensor signals simultaneously so that it can achieve robust performance and finally realize accurate induction motor fault recognition. Firstly, the acquired sensor signals are converted to time-frequency distribution (TFD) by wavelet transform. Then a deep convolutional neural network is applied to learning discriminative representations from the TFD images. After that, a fully connected layer in deep architecture gives prediction of induction motor condition based on learned features. In order to verify the effectiveness of the designed deep model, experiments are carried out on a machine fault simulator where both vibration and current signals are analyzed. Experiment results indicate that the proposed method outperforms traditional fault diagnosis methods, hence demonstrating effectiveness in induction motor application. Compared with conventional methods that rely on delicate features extracted by experienced experts, the proposed deep model is able to automatically learn and select suitable features that contribute to accurate fault diagnosis. Compared with single-signal input, the multi-signal model has more accurate and stable performance and overcomes the overfitting problem to some degree.

As a rising star in the field of deep learning, the Transformers have achieved remarkable achievements in numerous tasks. Nonetheless, due to the safety considerations, complex environment, and limitation of deployment cost in actual industrial production, the algorithms used for fault diagnosis often face the three challenges of limited samples, noise interference, and lightweight, which is an impediment in the fault diagnosis practice of transformer with high requirements for number of samples and parameters. For this reason, this article proposes a lightweight transformer based on convolutional embedding and linear self-attention (LSA), called CLFormer. By modifying the embedding module and the form of self-attention, the aim of lightweight is realized (MFLOPs: 0.12; Params: 4.88 K) under the condition of boosting high-accuracy of transformer. The effectiveness was demonstrated on Self-Made dataset with four comparative models, especially when each type of training sample is $6\times 4$ , the CLFormer achieves the highest average accuracy of 83.58% when the signal-to-noise ratio (SNR) is from −8 to 8 dB for three types of noise. As the first attempt to use transformer for fault diagnosis of rotating machinery, this work provides a feasible strategy for the research topic of fault diagnosis with the goal of practical deployment.

As one of the key components in mechanical systems, rotatory machine plays a significant role in safe and stable operation. Accurate prediction of the Remaining Useful Life (RUL) of rotatory machine contributes to realization of intelligent operation and maintenance for mechanical manufacturing. In order to overcome the limitations of traditional machine learning algorithms in dealing with complex nonlinear signals, a novel prediction framework for RUL of rotatory machine based on deep learning is proposed in this paper. One-dimensional convolutional neural network is utilized to extract local features from the original signal sequence. In addition, the proposed framework analyzes sensor signals and predicts RUL by combining Long Short-Term Memory (LSTM) network with attention mechanism. Multi-layer LSTM is set up to extract useful temporal features layer by layer and improve the robustness of the model, while attention mechanism is able to effectively solve the problem of information loss in the long-distance signal transmission of LSTM. Through the feature extraction of multi-layer LSTM and the strong supervision ability of attention mechanism, the RUL of rotatory machine can be accurately predicted. The experimental results show that the proposed method for RUL estimation is efficient and has higher prediction accuracy than the traditional machine learning algorithms.

High precision and fast fault diagnosis is an important guarantee for the safe and reliable operation of machinery. In recent years, due to the strong recognition ability, data-driven fault diagnosis technology based on deep learning has attracted enormous attention. The fault diagnosis module proposed by many scholars has achieved excellent recognition results, but some of them are too complex to deploy in practice, due to the high costs. In this article, an efficient feature extraction method based on the convolutional neural networks (CNN) was proposed, and the high-precision fault diagnosis task was completed used a lightweight network. First, a 1-D CNN (1-D-CNN) is used to extract the multichannel features for the input original signals, to generate the feature maps which are highly consistent with the 2-D CNN (2-D-CNN). Second, a strategy of the lightweight model is proposed based on the principle of dynamic convolution and separable convolution. Third, a spatial attention mechanism (SAM) is used to adjust the weights of the output feature maps. Finally, the model completes the diagnosis task through the learned features. The performance of the model was verified under different operating conditions and noise environments. The experimental results demonstrate that the proposed method has excellent anti-noise ability and domain adaptability.