# 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 feature map is then fed into a deep convolutional network (ConvNet) to learn distinguishable features, and ConvNet will directly link the final convolutional layer as input to the multiscale layer to keep global and local information. Experimentally, the strategy is shown to improve classification accuracy for metaclassifier main bearing fault diagnosis feature clustering. [PERSON] et al. [38] utilized the short-time Fourier transform to transform a one-dimensional vibration signal into a time-frequency domain picture. A CNN model comprised of four convolutional layers, two pooling layers, and two fully connected layers was developed for image classification-based health monitoring of asynchronous motor operating conditions. In fact, CNNs have powerful signal processing and analysis capabilities. If a one-dimensional signal is utilized directly as its input, it is possible to merge classical signal processing and feature extraction techniques to accomplish the most direct fault diagnosis and optimize the condition monitoring process. To incorporate a one-dimensional signal as input in a CNN, the CNN network topology must be modified, and a one-dimensional convolution kernel must be used. Many one-dimensional CNN models have been developed and implemented in equipment condition monitoring at present [152; 153; 154; 155; 156; 157; 158; 159]. [PERSON] et al. [160] employed vibration data derived from multiple sensors as input to a multi-headed one-dimensional CNN model to diagnose faults in induction motors. The one-dimensional CNN in each head consists of batch normalization, two convolutional layers, two pooling layers, a fully connected layer, and a softmax layer, with each output corresponding to a motor operating condition. The approach produces a diagnosis accuracy of 99.92% through experimentation and parameter optimization, and the network is quick to train and test. [PERSON] et al. [161] used raw vibration signals of each fault pattern to train a one-dimensional CNN model to detect and recognize data features. The acquired features were then utilized to train an HMM classifier for fault diagnosis. The classification results were compared with CNN, SVM and BP neural networks and showed that the model gave accurate classification for different bearing datasets. [PERSON] et al. [162] suggested a multi-scale kernel residual CNN (MK-ResCNN) for diagnosing motor faults. The vibration signals from various motor running states were segmented into fragment samples to build the training and test sets of the CNN model, enabling the distinction and classification of fault features. Residual learning is included into the multi-scale kernel CNN to prevent degradation problems in deep networks. The approach eliminates the requirement for converted data, and the results demonstrate that reliable classification of noisy fault data can be performed under non-stationary operating conditions by evaluating a normal motor and five faulty motors. The relevant references for the CNN methods discussed in this section are summarized in Tables 18-20.