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Machine learning techniques for fault detection and diagnosis
In fault detection and diagnosis, mathematical classification models which in fact belong to supervised learning methods are trained on the training set of a labeled dataset to accurately identify the redundancies, faults and anomalous samples. During the past decades there are quite different classification and preprocessing models developed and proposed in this research area. k-nearest-neighbors algorithm(kNN) is one of the oldest techniques which have been used to solve fault detection and diagnosis problems. Despite the simple logic that this instance-based algorithm has, there are some problems with large dimensionality and processing time when it is used on large datasets. Since kNN is not able to automatically extract the features to overcome curse of dimensionality, so often some data preprocessing techniques like Principal component analysis(PCA), Linear discriminant analysis(LDA) or Canonical correlation analysis(CCA) accompany it to reach a better performance. In many industrial cases the effectiveness of kNN has been compared with other methods, specially with more complex classification models such as Support Vector Machines(SVMs), which is widely used in this field. Thanks to their appropriate nonlinear mapping using kernel methods, SVMs have an impressive performance in generalization, even with small training data. However, general SVMs do not have automatic feature extraction themselves and just like kNN, are often coupled with a data pre-processing technique. Another drawback of SVMs is that their performance is highly sensitive to the initial parameters, particularly to the kernel methods, so in each signal dataset a parameter tuning process is required to be conducted firstly. Therefore, the low speed of the training is a limitation of SVMs to be used in some fault detection and diagnosis cases.

Artificial Neural Networks(ANNs) are among the most mature and widely used mathematical classification algorithms in fault detection and diagnosis. ANNs are well-known for their efficient self-learning capabilities of the complex relations (which generally exists inherently in fault detection and diagnosis problems) and are easy to operate. Another advantage of ANNs is that they perform automatic feature extraction by allocating negligible weights to the irrelevant features, helping the system to avoid dealing with another feature extractor. However, ANNs tend to over-fits the training set, which will have consequences of having poor validation accuracy on validation set. Hence, often, some regularization terms and prior knowledge are added to the ANN model to avoid over-fiting and achieve higher performance. Moreover, properly determining the size of the hidden layer needs an exhaustive parameter tuning, to avoid poor approximation and generalization capabilities. In general, different SVMs and ANNs models (i.e. Back-Propagation Neural Networks and Multi-Layer Perceptron) have shown successful performances in the fault detection and diagnosis in industries such as gearbox, machinery parts (i.e. mechanical bearings ), compressors , wind and gas turbines and steel plates.

Deep learning techniques for fault detection and diagnosis


Recently, by research advances in ANNs and advent of deep learning algorithms, using deep and complex layers, novel classification models are developed to cope with fault detection and diagnosis. Most of the shallow learning models extract a few feature values from signals, causing a dimensionality reduction from the original signal. While, by using a Convolutional neural networks, the continuous wavelet transform scalogram can be directly classified to normal and faulty classes. Such a technique avoids omitting any important fault message and results in a better performance of fault detection and diagnosis. In addition, by transforming signals to image constructions, a 2D Convolutional neural networks can be implemented to identify faulty signals from vibration image features.

Deep belief networks, Restricted Boltzmann machines and Autoencoders are other deep neural networks architectures which have been successfully used in this field of research. Comparing to traditional machine learning, due to the deep architecture, deep learning models are able to learn more complex structures from datasets, however they need larger samples and longer processing time to achieve higher accuracy.