# 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
\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
[164] & \multirow{2}{*}{Modelling} & Heat & Temperature & Clogging fault \\  & & pump & Multiple & \\
[173] & \multirow{2}{*}{Fault} & Power & sensors & N/A \\  & & prognosis & pump & monitoring data & \\
[174] & \multirow{2}{*}{Remaining} & Electromagnetic & \multirow{2}{*}{Vibration and pressure signal} & \multirow{2}{*}{Cavitation} \\  & & useful life & pump & Multiple & \\
[175] & \multirow{2}{*}{Future state} & Water injection & sensors & \\  & & pump & monitoring data (vibration, & N/A \\  & & temperature, flow, pressure, distance) & \\
[92] & \multirow{2}{*}{Fault} & Reciprocating & Vibration & Piston wear, bearing wear, and \\  & diagnosis & pump & signal & valve disc wear faults \\
[185] & \multirow{2}{*}{Remaining} & Hydraulic gear & Vibration, flow, and pressure signal & Fuel contamination \\
[188] & \multirow{2}{*}{State trend} & Aircraft & \multirow{2}{*}{Accumulator pressure data} & N/A \\
[81] & \multirow{2}{*}{Fault} & Reactor coolant & Shaft seal & \\  & prognosis & pump & leakage flow & \\
[189] & \multirow{2}{*}{Condition} & Main & \multirow{2}{*}{Temperature and leakage flow} & \multirow{2}{*}{Bearing wear fault} \\  & prediction & pump & & \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Pump condition monitoring using recurrent neural network. \begin{table}
\begin{tabular}{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
[163] & \multirow{2}{*}{Fault detection} & Induction & Current & \\  & and isolation & motor & signal & \\
[168] & \multirow{2}{*}{Fault detection} & Brushless DC & Current and & \\  & and diagnosis & motor & vibration signal & \\
[171] & \multirow{2}{*}{Fault} & Induction & \multirow{2}{*}{Rotating sound signal} & \multirow{2}{*}{Bearing fault (Ball, inner race, and \\  & diagnosis & motor & & \\
[172] & \multirow{2}{*}{Fault diagnosis} & Induction & Vibration and current signal & \\  & & motor & & \\  & & Permanent & Three-phase current signal & open and shot circuit fault \\
[183] & \multirow{2}{*}{Fault detection} & Magnet & and rotor & (instantaneous fault or gradual fault), \\  & & Synchronous & position & and winding resistance increase or \\  & & Motor & information & \\
[184] & \multirow{2}{*}{Fault diagnosis} & Three-phase & \multirow{2}{*}{Vibration} & \multirow{2}{*}{Vollage imbalance, rotor imbalance,} \\  & & asynchronous & signal & faulty bearing, broken rotor bars, and \\  & & motor & & \\
[186] & \multirow{2}{*}{Remaining} & Induction & \multirow{2}{*}{Current and voltage signal} & \multirow{2}{*}{Bearing fault} \\  & & prediction & motor & \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Motor condition monitoring using recurrent neural network.