# 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
In [118], when the current and vibration signals of the induction motor have been gathered, statistical characteristics are derived from their respective time domain data. Afterwards, the most effective features and the optimal SVM model parameters for fault diagnosis were chosen using a wraparound model and a grid search technique. Through fault diagnosis and prediction for diverse operating conditions of induction motors at different speeds and loads, a high level of precision can be achieved. The performance of diagnosis at an intermediate speed and load (which reflect the scenario of limited data) is also encouraging. [PERSON] et al. [119] classified statistical characteristics taken from a centrifugal pump's time-domain vibration data using SVM. Experiments were undertaken to identify two kinds of faults: flow obstructions and pump cavitation, each with a different fault severity and conditions. The results indicate that the SVM model gives more accuracy in predicting the beginning of cavitation events than the multi-class fault classification for different degrees of blockages. In [120], the time and frequency domain features of the bearing vibration signal are used to train an SVM model. The trained classifier can be used for real-time detection of inner race, outer race and rolling element faults in bearings. The Root mean square of vibration signal (RMS) features in the time domain are used for fault detection, while the energy content and energy deviation features in the frequency domain are used for fault classification. The experimental results show an accuracy of 86% and 96% for the detection of inner and outer race faults respectively. There are also modifications of the SVM, and optimized algorithms applied to the condition detection of equipment. For instance, in [60], [PERSON] et al. used a fuzzy support vector machine (FSVM) to increase sensitivity to data and model generalization for the assessment of static and dynamic eccentricity fault severity in permanent magnet synchronous motors. The wavelet transform analysis of stator current features was used as training and testing data for the FSVM classifier, and it was determined that the model could accurately identify the type and degree of eccentricity and had a classification accuracy of 100% even in the presence of high noise. Since SVM can only perform binary classification, [PERSON] et al. [121] have employed multiclass support vector machines (MSVM) to effectively diagnose and predict a broad variety of mechanical and electrical faults in induction motors. The time domain statistical features extracted from the current and vibration signals of the induction motor were then used to train the MSVM classifier. Several experiments demonstrate that the model can accurately predict all mechanical and electrical problems under varying loads. Some researchers also optimize the SVM parameters to achieve improved classification results. In [61], the frequency domain and wavelet analysis features of the vibration signals were extracted from various fault conditions of the pump under various operating conditions. These features serve as inputs to the SVM classifier to classify pump pipe blockage and cavitation faults. In addition, three optimization algorithms were used to determine the SVM classifier's parameters for fault classification accuracy at higher pump speeds. The relevant references for the SVM methods discussed in this section are summarized in Tables 13-15.