# 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
**References** & **Application** &
\begin{tabular}{c} **Type of** \\ **Equipment** \\ \end{tabular} & **Signal** & **Fault Type** \\ \hline
[38] & Fault & Asynchronous motor & Vibration & Built-in rotor imbalance, stator winding faults, built-in faulted bearing, built-in bowed rotor, built-in broken rotor bars, and voltage imbalance and single phasing \\
[152] & Fault & Induction & Current & Bearing fault \\
[153] & detection & motor & signal & Bearing fault \\
[153] & Fault & Permanent magnet & Current & Bearing fault \\
[160] & diagnosis & synchronous motor & signal & Demagnetization fault and bearing fault \\
[160] & Fault detection & Induction & Vibration & Bent shaft, broken bar, misalignment, mechanical looseness, bearing fault and unbalance \\
[162] & and diagnosis & motor & signal & Bowed rotor, broken rotor bar, faulty bearing, high impedance and, unbalance rotor \\ \hline
[160] & Fault & Induction & Vibration & Bowed rotor, broken rotor bar, faulty bearing, high impedance and, unbalance rotor \\
[162] & diagnosis & motor & signal & high impedance and, unbalance rotor \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Motor condition monitoring using convolutional neural network. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Application** &
\begin{tabular}{c} **Type of** \\ **Equipment** \\ \end{tabular} & **Signal** & **Fault Type** \\ \hline
[36] & Fault & Axial piston pump & Vibration & Cavitation (different severity) \\  & diagnosis & & Vibration, & Swash plate wear, \\
[55] & Fault & Hydraulic pump & pressure, and & Loose slipper, \\  & diagnosis & & sound signal & Slipper wear, \\
[150] & Fault pattern & Water & Vibration & \\  & recognition & pump & signal & \\
[156] & Fault & Centrifugal pump & Vibration & Cavitation, impeller unbalance, and \\  & diagnosis & Centrifugal pump & signal & shaft misalignment \\
[157] & Condition & Hydraulic pump & Vibration & High temperature influence on the \\  & monitoring & & signal & volumetric efficiency \\
[158] & Fault & Hydraulic pump & Vibration & Slipper failure, loose slipper, swash plate \\  & diagnosis & & signal & wear, and central spring wear \\
[159] & Fault & Hydraulic pump & Pressure & Swash plate wear, loose slipper failure, \\  & diagnosis & & signal & slipper wear, and central spring wear \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Pump condition monitoring using convolutional neural network. Recurrent neural networks (RNN) are a framework for processing sequential data, remembering the previous information through the connection structure between each layer and using this information to influence the output of later nodes. RNNs can fully exploit the temporal and semantic information in sequential data, and this approach is more capable of deeper representation than full connected neural networks and CNNs in processing temporal data. A typical recurrent neural network structure is shown in Figure 6. For ease of understanding, the RNN structure in Figure 6 is expanded dutifully in a time series as shown in Figure 7. It is worth noting that the nodes in the recurrent layer of the RNN represent a hidden layer state rather than a neuron.