Compound fault diagnosis plays a critical role in lowering the maintenance time and cost of industrial robots. With the advance of deep learning and industrial big data, the compound fault diagnosis model can be established through a data-driven approach. However, current methods mainly focus on the single fault diagnosis of assets, which cannot achieve satisfactory performance of compound fault diagnosis. This study proposes a compound fault diagnosis algorithm for the industrial robot based on multimodal feature extraction and fusion. Firstly, the multi-head self-attention enhanced convolution neural network (CNN) module and long short-term memory (LSTM) network module are adopted to learn the fault-related features from different perspectives simultaneously. The local and global features extracted by the aforementioned modules are then fused for subsequent compound fault classification. An experimental study was implemented based on the real-world robotic sensor data. The experimental results indicated that the proposed multi-modal algorithm shows merits in compound fault diagnosis in comparison with other state-of-the-art methods.
Aiming at the problems of high manual cost, low efficiency, and low precision of the mechanical axis health management in industrial robot applications, this paper proposes a health assessment and state prediction algorithm based on hidden Markov model (HMM) and temporal convolutional networks (TCN). First, the MPdist similarity comparison algorithm is used to construct the mechanical axis health index. Then the hidden Markov model is trained with observable sensor data. After that, the temporal convolution neural network is used to predict state transition time iteratively, and the predicted results are decoded by HMM. The experimental results show that the HMM‐TCN model can accurately assess the health state of the mechanical axis and predict the state transition in real‐time. The prediction accuracy of this method reaches 87.5%, and the error interval locates in [−3,9] time steps. The accuracy, early/late prediction indicators are better than HMM‐RNN, HMM‐LSTM, and HMM‐GRU.
Aiming at the problems of complex structure, high components coupling, and difficultly monitoring of the whole health status with the industrial robot, a metric learning-based whole health indicator model is proposed.First, according to the more obvious degradation characteristics of industrial robots during accelerated operation, the accelerated signal is segmented and then the time-domain features are extracted. Second, the longterm and short-term memory (LSTM) network combined with the multihead attention is used to construct the network model, and the metric learning method is adopted to learn the similarity measurement method of the industrial robot monitoring data. Finally, the similarity measure method got from metric learning is used to construct the whole health indicator, which describes the whole degradation trend of the industrial robot.The experiments are based on the real accelerated aging data set from industrial robots. The results show that the proposed model can effectively construct the whole health indicator for industrial robots. The average trend of the proposed model reaches 0.9769. The average monotonicity reaches 0.5666, which is 0.1748, 0.1577, and 0.1492 higher than the similarity measurement method based on Euclidean distance, Markov distance, and LSTM.
Fault diagnosis is an important link in intelligent development of industrial robots. Aiming at the problem of weak fault diagnosis performance caused by insufficient training samples, a fault diagnosis model based on triplet network is proposed. Firstly, we combine the multiscale convolutional neural network (MSCNN) with channel attention networks (squeeze-and-excitation network, SENet), and use it to construct a triple sub-network structure MS-SECNN, which can adaptively extract features from the original fault signal. Then, the feature similarity is calculated by triplet loss in the low dimensional space to realize the fault classification task. The experiments are based on the real industrial robot operation data set. In this model, we use Few-shot learning strategy to test the diagnostic performance under small samples, and compare it with WDCNN, FDCNN and MSCNN models. Experimental results show that the proposed model has more effective fault classification ability under small samples. In addition, when the training sample size is 1400, the average accuracy of MS-SECNN reaches 99.21%.
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