# 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  
Most machine learning approaches have a shallow structure, often including one hidden layer, with strong self-learning capabilities, nonlinear mapping abilities, and high \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Application**} & \multicolumn{2}{c}{**Type of Equipment**} & \multirow{2}{*}{**Signal**} & \multirow{2}{*}{**Fault Type**} \\ \hline
[68] & Fault & Airborne & Vibration and & Blade damage, diffusion tube damage, leakage, \\ diagnosis & fuel pump & pressure signal & diffusion tube impeller rub, and bearing wear \\
[72] & Feature & Nuclear & Vibration & Rolling bearing fault \\ extraction & main pump & signal & & \\
[74] & Fault & Vibration & & \\
[74] & diagnosis & Hydraulic pump & signal & \\
[77] & Fault & Gear & Vibration & \\
[78] & diagnosis & pump & signal & \\
[81] & Fault & Reactor & Shaft seal & \\  & prognosis & coolant pump & leakage flow & \\  & & & & \\  & & & & \\
[82] & Fault & Vacuum & Acoustic & extracting the atmosphere at full power is \\  & diagnosis & pump & emission signal & considered an overload fault and extracting the \\  & & & & pressure vessel through the aperture is \\  & & & & considered normal.) \\
[84] & Fault & Aviation & Discharge & \\  & detection & piston pump & pressure signal & \\
[91] & Fault & Hydraulic pump & Vibration & Single slipper wear, single slipper loose, and \\  & diagnosis & Hydraulic pump & signal & center spring wear faults \\
[92] & Fault & Reciprocating & Vibration & \\  & diagnosis & pump & signal & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Application**} & \multicolumn{2}{c}{**Type of Equipment**} & \multirow{2}{*}{**Signal**} & \multirow{2}{*}{**Fault Type**} \\ \hline
[69] & Fault & Rolling & Vibration & Outer race, inner race, and ball faults \\
[70] & diagnosis & bearing & signal & \\
[73] & Fault detection & Bearing in main & Vibration & Outer race, inner race, and ball faults \\  & and diagnosis & coolant pump and & signal & Inner race, outer race, and ball faults \\  & & feed water pump & & \\  & & & & \\  & & & & \\
[78] & Fault diagnosis & Locomotive roller & Vibration & fault in the inner race, roller rub fault, compound \\  & diagnosis & bearing & signal & faults in the outer and inner races, compound \\  & & & & \\  & & & & faults in the inner race and rollers, compound \\
[79] & Fault & Rolling & Vibration & Outer race, inner race, and ball faults \\
[80] & Fault & Rolling & Vibration & Outer race fault and ball faults \\  & diagnosis & bearing & signal & Outer race fault and ball faults \\
[83] & Fault diagnosis & Rolling & Vibration & Outer race, inner race, and ball faults \\  & and prognosis & bearing & signal & Outer race, inner race, and ball faults \\
[85] & Fault & Rolling & Vibration & Inner race, outer race and rolling element faults \\
[86] & Analysis & bearing & signal & Inner race, outer race faults \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Bearing condition monitoring using empirical model decomposition. resilience for feature extraction and pattern analysis. Artificial neural networks (ANN), support vector machines (SVM), and extreme learning machines (ELM) are common shallow machine learning techniques. A typical neural network structure is shown in Figure 3.  
The Artificial Neural Networks Approach  
ANN is a data processing model like the human nervous system, which includes neuron nodes and weights. The nodes are the basic processing units and are arranged into layers. Neurons in the input layer receive inputs from the outside world and neurons in the output layer give out the neural network processing results. The layers between the input and output layers are called hidden layers and the neurons in these layers are known as hidden neurons.