# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Fault Diagnosis and Prediction
#### 2.2.2 Method Based on Deep Learning
The health states were then categorized using a DBN classifier, and the method's validity was validated using two datasets, a power transformer and an aviation engine. In various fields, such as motors, pumps, and bearings, study and application of DBN-based condition monitoring techniques are expanding [130; 131]. In [132], a DBN-based fault diagnostic approach for induction motors is proposed. The motor vibration signal is used as input for feature learning by the DBN classifier. The acquired features are then utilized to train a BP neural network for fault identification. The results were verified using time-domain analysis and wavelet transform, which revealed that the DBN-learned features attained the maximum classification accuracy of 95.8%. [PERSON] et al. [133] implemented a DBN for axial piston pump fault diagnosis. First, the time-domain, frequency-domain, and time-frequency-domain data indicators of the original vibration signals of the faults were computed. The data metrics are then used as training samples and input to the DBN, which is trained layer by layer to achieve classification and detection of multiple faults. By comparison with SVM and ANN, it was determined that the fault features automatically learnt by DBN could reach a classification accuracy of 97.4%. In [134], DBN is used to improve rolling bearing fault diagnosis. The DBN classifier was trained and evaluated using time-domain characteristics of vibration signals from multiple kinds of faults. The results indicate that a diagnostic accuracy of 97.5% is achieved using the multi-signal fusion technique, as opposed to employing a single fault signal. Moreover, compared to SVM, KNN, and BP neural networks, DBN has the highest average classification accuracy at 93.17%. In the present day, DBNs have been successfully used to fault classification and diagnostic problems [135; 136], and in recent years, DBNs have been utilized by combining them with other approaches. For example, in [137], [PERSON] et al. proposed a combination of adaptive DBNs and dual-tree complex wavelet packets (DTCWPT). DTCWPT was used to decompose the original bearing vibration signal into eight distinct frequency band components, from which nine statistical features were recovered to generate a feature set. A 5-layer adaptive DBN is then trained these features to classify faults. Comparing the method to other traditional machine learning methods using the same data revealed that the method had the highest average accuracy, 94.38%. [PERSON] and [PERSON] [138] also propose a method for fault diagnosis of bearings using a combination of sparse autoencoder (SAE) network and DBN. First, statistical features are derived from the time and frequency domains of various vibration signals. Multiple two-level sparse self-encoders (SAEs) are then fed these features to learn higher-level features. Based on the fused feature vectors, DBN is then utilized to identify bearing faults. Recent research has begun to improve the diagnostic and predictive performance of DBN models by optimizingparameters and adjusting internal structure of the model [139; 140; 141; 142]. [PERSON] et al. [143] proposed a Bispectrum entropy and DBN-based method for enhancing the performance of fault prognosis for hydraulic pumps. First, a Bispectrum analysis of the pump vibration signal was carried out to extract 15 Bispectrum entropies in distinct frequency bands as predictive features. The normalized features are then utilized as training data for a 3-layer DBN network to learn features. Using the quantum particle swarm optimization (QPSO) approach, the DBN model parameters are optimized to increase prediction accuracy. In conclusion, a comparison with conventional approaches (SVM and ANN models) demonstrates that the method can reliably anticipate trends and random fluctuations in hydraulic pump performance deterioration with good generality and prediction accuracy. To increase the accuracy of continuous data modelling, [PERSON] et al.