# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Data Processing Methods
#### 2.1.2 The Wavelet Transform Approach
Subsequently, a principal component analysis (PCA) model was constructed from the low frequency features obtained under normal operating conditions to accurately detect single and multiple faults (cavitation, impeller faults and combinations of cavitation and impellerfaults) in centrifugal pumps, and analysis of the principal component scores and load maps enabled fault identification. [PERSON] et al. [57] also used DWT to perform a six-level decomposition of the vibration signal of a centrifugal pump, extracting 60 approximate and detailed features for different mother wavelet functions, and applied them to train a multilayer perceptron-based backpropagation neural network and support vector machine model. It was found that with the use of rbio1.5 mother wavelet decomposition, not only fewer feature parameters could be obtained, but also the accuracy of the fault detection and diagnosis model could be achieved at 100%. In [58], the EWT was used to extract the modes of the bearing vibration signals of the motor and their corresponding envelope spectra, and the corresponding fault eigenfrequencies obtained were used to implement fault detection. By comparing and analyzing the effectiveness of EWT with empirical mode decomposition (EMD) using simulated and real data, it was found that EWT has better performance in weak feature detection and composite fault detection, while EMD can only extract one temporal frequency of the fault feature frequencies and cannot detect faults under strong noise interference. [PERSON] et al. [59] applied EWT to analyze the Fourier spectrum segments of motor vibration signals extracted by Fast Fourier Transform (FFT). The time domain contribution of the corresponding spectral bands was also obtained by inverse Fourier transform and finally the root mean square value of the time domain signal was calculated to achieve motor bearing fault detection. The corresponding references for the WT methods reviewed in this section are summarized in Tables 4-6 below. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Application** &
\begin{tabular}{c} **Type of** \\ **Equipment** \\ \end{tabular} & **Signal** & **Fault Type** \\ \hline
[30] & Fault diagnosis & \begin{tabular}{c} Induction \\ motor \\ \end{tabular} & \
\begin{tabular}{c} Current \\ signal \\ \end{tabular} &
\begin{tabular}{c} Rotor and bearing fault \\ \end{tabular} \\
[43] & Fault detection & \begin{tabular}{c} motor \\ \end{tabular} & \
\begin{tabular}{c} Signal \\ \end{tabular} &
\begin{tabular}{c} Air gap eccentricity fault \\ \end{tabular} \\
[45] & Fault detection & \begin{tabular}{c} motor \\ \end{tabular} & \
\begin{tabular}{c} Signal \\ \end{tabular} &
\begin{tabular}{c} Broken rotor bar fault \\ \end{tabular} \\
[48] & Fault diagnosis & \begin{tabular}{c} motor \\ \end{tabular} & \
\begin{tabular}{c} Signal \\ \end{tabular} &
\begin{tabular}{c} Rotor and bearing faults \\ \end{tabular} \\
[49] & Fault & \begin{tabular}{c} Permanent magnet \\ \end{tabular} & \
\begin{tabular}{c} Current \\ signal \\ \end{tabular} &
\begin{tabular}{c} Broken magnet and eccentricity faults \\ \end{tabular} \\
[59] & Fault detection & \begin{tabular}{c} motor \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Bearing fault \\ \end{tabular} \\
[60] & Fault diagnosis & \
\begin{tabular}{c} Permanent magnet \\ synchronous \\ \end{tabular} &
\begin{tabular}{c} Voltage and \\ speed \\ \end{tabular} & Static and dynamic eccentricity fault \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Motor condition monitoring using wavelet transform.