# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Fault Diagnosis and Prediction
#### 2.2.1 Method Based on Shallow Machine Learning
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Application** &
\begin{tabular}{c} **Type of** \\ **Equipment** \\ \end{tabular} & **Signal** & **Fault Type** \\ \hline
[35] & Fault diagnosis & \begin{tabular}{c} Plunger \\ pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Swash plate wear and rotor wear \\ \end{tabular} \\
[57] & Fault diagnosis & \begin{tabular}{c} Centrifugal \\ pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Five mechanical faults (bearing, misalignment, \\ unbalance, impeller, and looseness), and a hydraulic \\ fault (cavitation) \\ \end{tabular} \\
[61] & Fault diagnosis & \begin{tabular}{c} Centrifugal \\ pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Suction flow blockages \\ cavitations \\ \end{tabular} \\
[84] & Fault diagnosis & \begin{tabular}{c} Aviation piston \\ diagnosis \\ \end{tabular} & \
\begin{tabular}{c} Discharge \\ pressure signal \\ \end{tabular} &
\begin{tabular}{c} loose piston defect \\ \end{tabular} \\
[110] & Fault diagnosis & \begin{tabular}{c} Oil rig motor \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Misalignment, structural looseness, unbalance, \\ hydrodynamic, mechanical looseness, rolling bearing \\ Cavitation and impeller unbalance, cavitation and \\ shaft misalignment, impeller unbalance and shaft \\ misalignment \\ \end{tabular} \\
[111] & \begin{tabular}{c} Fault \\ diagnosis \\ \end{tabular} & \begin{tabular}{c} Centrifugal \\ pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Severe or moderate degradation and normal or \\ good condition \\ \end{tabular} \\
[112] & Fault diagnosis & \begin{tabular}{c} Centrifugal \\ system \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Severe or moderate degradation and normal or \\ good condition \\ \end{tabular} \\
[115] & Condition evaluation & \begin{tabular}{c} Centrifugal \\ system \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Severe or moderate degradation and normal or \\ good condition \\ \end{tabular} \\
[116] & Condition prediction & \
\begin{tabular}{c} Reactor coolant \\ pump \\ \end{tabular} &
\begin{tabular}{c} Measurement \\ variables \\ \end{tabular} & Variables out of control after a fault occurred \\
[117] & Fault diagnosis & \begin{tabular}{c} Feed water \\ pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Initial imbalance, rotor misalignment, rotor axial \\ rubbing, thrust bearing damage, bearing looseness, \\ bearing stiffness vary, foundation resonance, \\ coupling damage \\ \end{tabular} \\
[119] & Fault prognosis & \begin{tabular}{c} Centrifugal \\ pump \\ \end{tabular} & \
\begin{tabular}{c} Vibration \\ signal \\ \end{tabular} &
\begin{tabular}{c} Flow blockages and cavitation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Pump condition monitoring using support vector machine. The Extreme Learning Machine Approach  
In traditional neural network training algorithms, many iterations of training are involved. This parameter adjustment process is not only time-consuming, but also computationally intensive, resulting in low network training efficiency. In the implementation of the algorithm, it is easy to generate local minima and has poor applicability. To address the above problems, [PERSON] et al. [13]. proposed the ELM neural network. In contrast to previous single-hidden-layer feedforward neural networks, the ELM randomly selects weights and biases in the hidden layer, and then calculates the weights in the output layer by a regularized linear regression method. The number of neurons in the input, hidden and output layers are \(n,\;1,\;m\), respectively.