Method based | Main weaknesses | |||
---|---|---|---|---|
Fuzzy logic [22] | 1. Necessity to large accessible data 2. Unpromising efficiency in inaccurate inputs 3. complete dependence on expertise and human intelligence | |||
Expert systems [23] | 1. Insufficient accuracy 2. Complexity 3. Low flexibility | |||
Evolutionary algorithms [24] | 1. Require long time 2. Unsuitable for online fault diagnosis | |||
Artificial neural network [25] | Conventional neural networks | RBF [26] | 1. Inability to self-learning without feature extraction 2. Necessity to large accessible data | |
MLP [26] | ||||
 | SVM [27] | 1. Necessity to large accessible data 2. Sluggish Performance due to large data requirement | ||
 | k-NN [28] | 1. Necessity to large accessible data 2. Slow performance in real time 3. Outlier-sensitivity | ||
 | ANN [29] | 1. Necessity to large accessible data 2. Overfitting | ||
Modern neural networks | Decision trees and random forest [30] | 1. Necessity to large accessible data 2. The longest training period 2. Overfitting | ||
 | Native Bayes [31] | 1. Necessity to large accessible data 2. Suitable only for independent features | ||
 | Deep learning | CNN [32] | 1. large data requirement 2. Need long time for training | |
 | RNN [33] | |||
 | AE [34] | |||
 | LSTM [35] |