# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Data Processing Methods
#### 2.1.1 The Fourier Transform Approach
After that, the time-frequency matrices of the signals in different operating states of the plunger pump were obtained, and the time-frequency entropy of each state was calculated once to extract the features. The obtained feature matrices are then dimensionally reduced by using principal component analysis (PCA). Finally, the processed signal features are used to train a support vector machine (SVM) classifier for the diagnosis of swash plate and rotor wear faults. In addition, STFT has also been applied to signal dimensional conversion. For example, [PERSON] et al. [36] converted the one-dimensional vibration signal of an axial piston pump into a two-dimensional spectrogram by STFT, and effectively improved the accuracy of the STFT spectrogram feature map by a denoising method, which was used as the input to a LeNet-5 convolutional neural network to effectively improve the ability to identify cavitation conditions in noisy environments. [PERSON] et al. [37] also used STFT to transform the 1D time-domain vibration signals of bearings into 2D time-frequency maps, and the multiple time-frequency maps obtained by adjusting the length of the STFT window were used as input to a classification generative adversarial network (CatGAN), which was trained to obtain a CatGAN model for diagnosing inner race and outer race faults. The corresponding references for the FT methods reviewed in this section are summarised in Tables 1-3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Application** & \multicolumn{2}{c}{**Type of Equipment**} & **Signal** & **Fault Type** \\ \hline
[26] & Fault diagnosis & Servo & Current & Axis misalignment (right and left axis with \\
[30] & Fault diagnosis & motor & signal & different amplitude) \\
[30] & Fault diagnosis & Induction & Current & \\
[31] & diagnosis & motor & signal & Rotor and bearing fault \\
[31] & Fault diagnosis & Brushless DC motor & Voltage & \\  & diagnosis & signal & & \\
[32] & Fault diagnosis & Induction & Current & \\  & diagnosis & motor & signal & \\  & & & & \\
[38] & Fault diagnosis & Asynchronous motor & Vibration & Built-in faulted bearing, built-in bowed rotor, \\  & diagnosis & signal & & built-in broken rotor bars, \\  & & & & voltage imbalance and single phasing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Motor condition monitoring using Fourier transform. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Application** & \multicolumn{2}{c}{**Type of Equipment**} & **Signal** & **Fault Type** \\ \hline
[27] & Feature extraction & Centrifugal & Vibration & \\  & extraction & pump & signal & Cavitation \\
[35] & Fault diagnosis & Plunger & Vibration & \\  & diagnosis & pump & signal & \\
[36] & Fault diagnosis & Axial pistonv & Vibration & \\  & diagnosis & pumps & signal & \\
[39] & Fault detection & Centrifugal & Vibration & \\  & detection & pump & signal & Cavitation \\
[40] & Fault detection & Centrifugal & Vibration & \\  & detection & pump & signal & \\
[41] & Fault detection & Gear & Vibration & \\  & detection & pump & signal & Abrupt changes in the behaviour caused by cavitation \\
[42] & Fault detection & Motor & Vibration & \\  & classification & pump & signal & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pump condition monitoring using Fourier transform.