# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 3 Conclusions  
In this paper, we review the progress of the last 10 years of research into data-driven condition monitoring applied to three types of commonly industrial equipment: motors, pumps, and bearings. The structure and applications of data-driven condition monitoring are investigated. The large number of reviewed works indicate that data-driven condition monitoring of industrial equipment is gaining popularity. The reported data-driven equipment condition monitoring approaches generally involve a feature extraction phase and fault classification/prediction phase. The most widely used feature extraction techniques include Fourier Transform (FT), Wavelet Transform (WT), and Empirical Modal Decomposition (EMD). The most widely used classification and prediction techniques include artificial neural networks (ANN), support vector machines (SVM) and extreme learning machines (ELM), deep belief networks (DBN), convolutional neural networks (CNN) and recurrent neural networks (RNN). As a result of the literature assessment shown above, the following list of unsolved critical future challenges is presented. * Literature has focused on various types of faults, including rotor, stator winding, bearing wear, unbalance faults, etc., in motors; cavitation, leakage, impeller faults, etc., in pumps; and inner race, outer race, ball, and roller faults in bearings. These faults are often assessed individually in condition monitoring, however, multiple faults can exist in a single component, so it is necessary to consider this situation carefully and to achieve differentiation and resolution of multiple faults. * As different types of equipment may have faults of varying severity in their operating condition, it is essential to consider the state of development of faults to correctly diagnose and detect them at the earliest stages of their occurrence, which is very rare in research work. * Another issue that cannot be disregarded is the imbalance of data categories, since equipment always operates under normal conditions to collect normal data, resulting in a small number of fault sample data. In addition, most AI-based monitoring systems utilize historical or current databases. However, it is impossible to have a database for \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
[176] & Fault & Rolling & Vibration & Ball, inner race, and outer race faults \\  & diagnosis & bearing & signal & Ball, inner race, and outer race faults \\
[177] & Fault & Rolling & Vibration & Ball, inner race, and outer race faults \\  & diagnosis & bearing & signal & Inner race, outer race and roller fault and combination of outer race and roller faults \\  & Condition & Rolling & Vibration & roller, inner race, and outer race fault \\  & monitoring & bearing & signal & \\
[180] & Fault & Aero engine & Vibration & Inner race, outer race, and ball faults \\  & prediction & bearing & signal & Inner race, outer race, and ball faults \\
[181] & Remaining & Rolling & Vibration & Inner race, outer race, and ball faults \\  & useful life & bearing & signal & Inner race, outer race, and ball faults \\  & Fault & Rolling & Vibration & \\  & diagnosis & bearing & signal & Inner race, outer race, and ball faults \\  & Prognosis and remaining & Rolling & Vibration & \\
[190] & useful life & bearing & signal & Inner race, outer race, and ball faults \\  & prediction & & & \\
[166] & Fault & Motor & Current and & \\  & detection & bearing & vibration signal & Air gap eccentricity and ball faults \\  & Fault & Rolling & Vibration & \\  & diagnosis & bearing & signal & Inner race, outer race, and ball faults \\
[182] & Fault & Rolling & Vibration & \\  & diagnosis & bearing & signal & Inner race, outer race, and ball faults \\ \hline \hline \end{tabular}
\end{table} 
Table 23: _Cont._all machines operating under all conditions.