# PSSS-MCVT paper reference & ch1,2
###### `NTUT` `PhD` `Paperdraft`
# Survey paper
* [A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10049129)[2023]

Author: Zhijun Ren , Tantao Lin

Key word: Data processing methods, imbalanced data,
intelligent fault diagnosis, model construction methods, training
optimization methods.

Abstract: The theoretical developments of data-driven fault diagnosis methods have yielded fruitful achievements and significantly benefited industry practices. However, most methods are developed based on the assumption of data balance, which is incompatible with engineering scenarios. First, the normal state accounts for the majority of the equipment’s lifespan; second, the probability of various faults varies, both of which result in an imbalance in the data. The consequence of data imbalance in intelligent fault diagnosis methods has attracted extensive attention from the research community, and a significant number of papers have been published. Nevertheless, a comprehensive review of achievements in this field is still missing, and the research perspectives have not been thoroughly investigated. To end this, we review and discuss all the research achievements in fault diagnosis under data imbalance in this survey, based on to the best of our knowledge. First, the existing imbalanced learning methods are classified into three categories: data processing methods, model construction methods, and training optimization methods. Then, the three methodologies are introduced and discussed in detail: the data processing method is to optimize the inputs of the intelligent fault diagnosis model so that the imbalance rate of the sample set involved in training is reduced; the model construction method is to design the structure and the features of the intelligent fault diagnosis model so that the model itself is resistant to the effects of imbalance; the training optimization method is an optimization of the training process for intelligent fault diagnosis models, raising the importance of the minority class in the training. Finally, this survey summarizes the prospects of the imbalanced learning problem in intelligent fault diagnosis, discusses the possible solutions, and provides some recommendations.
# Related work

## SF - statistical fearure based
* [FaultNet: A Deep Convolutional Neural Network for bearing fault classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9345676)[2021]

Author: Rishikesh Magar, Lalit Ghule

Key word: Convolutional Neural Network, FaultNet, Featurization, Machine Learning, __statistical features__.

Abstract: The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechanical systems. In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults. We also highlight the importance of using different signal processing methods and analyze their effect on accuracy for bearing fault detection. Apart from the traditional machine learning algorithms we also propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy. The distinguishing factor of this work is the idea of channels proposed to extract more information from the signal, we have stacked the ‘Mean’ and ‘Median’ channels to raw signal to extract more useful features to classify the signals with
greater accuracy.

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* [Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8320512)[2018]

Author: Liuyang Song, Huaqing Wang

Key word: — Condition monitoring, fault diagnosis, feature
extraction, signal denoising, vibration measurement, __statistical features__.

Abstract: This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and WPT. A decision tree is used to structure intelligent diagnosis rules in each step until the states are fully and automatically detected. The efficacy of this method was confirmed by applying it to an experimental low-speed rotation machine.

## SW + Gray image - sliding window
* [A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8114247) [2018]

Author: Long Wen, Xinyu Li

Key word: Convolutional neural network (CNN), datadriven, fault diagnosis, image classification, __sliding window__.

Abstract: Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

## SW + Graph construted - sliding window
* [Temporal-Spatio Graph Based Spectrum Analysis for Bearing Fault Detection and Diagnosis](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9014489) [2021]

Author: Teng Wang, Zheng Liu

Key word: Bearing, fault detection, fault diagnosis,
spectrum analysis, temporal-spatio graph, __sliding window__.

Abstract: This article suggests that the correlation information, hidden in spatial configuration and temporal dynamic of frequencies, is an important indication for bearing health condition. To consider this information, we extend graph-modeling strategy, and introduce a bearing fault detection and diagnosis technique based on temporal-spatio graph. First, short-time periodogram is extracted from vibration signal, and, then, modeled by a temporal-spatio graph. In fault detection phase, the spectrum of temporal channel graph is used to map short-time periodogram to acquire the so-called graph-mapped spectrum (GMS). The principal frequency in resulting GMS is found highly related with the health condition of monitored bearing. Thus, any change of health condition can be detected by checking this principal frequency over time. Once a fault is detected, the spatio channel graph is fed to K-nearest neighbor classifier, coupled with a specific graph distance metric, for fault type identification. Comprehensive experiments on two benchmarking datasets along with theoretical interpretation demonstrate the superiority of proposed method over state of the arts. The proposed temporal-spatio graph provides a significant extension of existing spectrum analysis for fault detection and diagnosis.

## SW + GAF - sliding window
* [Human Activity Recognition Based on Gramian Angular Field and Deep Convolutional Neural Network](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9234451)[2020]

Author: HONGJI XU , JUAN LI

Key word: Deep convolutional neural network, Gramian angular field, human activity recognition,
multi-source sensor data fusion, __sliding window__.

Abstract: With the development of the Internet of things (IoT) and wearable devices, the sensorbased human activity recognition (HAR) has attracted more and more attentions from researchers due to its outstanding characteristics of convenience and privacy. Meanwhile, deep learning algorithms can extract high-dimensional features automatically, which makes it possible to achieve the end-to-end learning. Especially the convolutional neural network (CNN) has been widely used in the field of computer vision, while the influence of environmental background, camera shielding, and other factors are the biggest challenges to it. However, the sensor-based HAR can circumvent these problems well. Two improved HAR methods based on Gramian angular field (GAF) and deep CNN are proposed in this paper. Firstly, the GAF algorithm is used to transform the one-dimensional sensor data into the two-dimensional images. Then, through the multi-dilated kernel residual (Mdk-Res) module, a new improved deep CNN network MdkResNet is proposed, which extracts the features among sampling points with different intervals. Furthermore, the Fusion-Mdk-ResNet is adopted to process and fuse data collected by different sensors automatically. The comparative experiments are conducted on three public activity datasets, which are WISDM, UCI HAR and OPPORTUNITY. The optimal results are obtained by using the indexes such as accuracy, precision, recall and F-measure, which verifies the effectiveness of the proposed methods.

## SW + numerical array - sliding window
* [Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8782830)[2020]

Author: Wanke Yu, Chunhui Zhao

Key word: Broad learning system, convolutional neural network, fault diagnosis, incremental learning, __sliding window__.

Abstract: Fault diagnosis, which identifies the root cause of the observed out-of-control status, is essential to counteracting or eliminating faults in industrial processes. Many conventional data-driven fault diagnosis methods ignore the fault tendency of abnormal samples, and they need a complete retraining process to include the newly collected abnormal samples or fault classes. In this article, a broad convolutional neural network (BCNN) is designed with incremental learning capability for solving the aforementioned issues. The proposed method combines several consecutive samples as a data matrix, and it then extracts both fault tendency and nonlinear structure from the obtained data matrix by using convolutional operation. After that, the weights in fully connected layers can be trained based on the obtained features and their corresponding fault labels. Because of the architecture of this network, the diagnosis performance of the BCNN model can be improved by adding newly generated additional features. Finally, the incremental learning capability of the proposed method is also designed, so that the BCNN model can update itself to include new coming abnormal samples and fault classes. The proposed method is applied both to a simulated process and a real industrial process. Experimental results illustrate that it can better capture the characteristics of the fault process, and effectively update diagnosis model to include new coming abnormal samples, and fault classes.

## SS - systemetic sempling
* [Composite Fault Diagnosis of Rolling Bearing Based on Chaotic Honey Badger Algorithm Optimizing VMD and ELM](https://www.mdpi.com/2075-1702/10/6/469)[2022]

Author: Jie Ma, Sen Yu

Key word: honey badger algorithm, chaotic mapping, fault diagnosis ,extreme learning machin, Gini index of square envelope, __systemetic sampling__.

Abstract: In order to effectively extract the characteristic information of bearing vibration signals and improve the classification accuracy, a composite fault diagnosis method of rolling bearing based on the chaotic honey badger algorithm (CHBA), which optimizes variational mode decomposition (VMD) and extreme learning machine (ELM), is proposed in this paper. Firstly, aiming to solve the problem that the HBA optimization process can easily fall into local optimization and slow convergence speed, sinusoidal chaotic mapping is introduced to improve HBA, and the advantages of CHBA are verified by 23 benchmark functions. Then, taking the Gini index of the square envelope (GISE) as the fitness function, the VMD is optimized with CHBA to obtain the optimal number of modes K and the quadratic penalty factor. Secondly, the first four IMF components with the largest GISE values are selected, and the IMF components are grouped by the “Systematic Sampling Method (SSM)” to calculate the signal energy to form the fault feature vector. Finally, taking the classification error rate as the fitness function, the feature vector is input into the ELM model optimized by CHBA to classify and identify different types of faults. Through experimental analysis, and compared with BP, ELM, GWO-ELM, and HBA-ELM, this method has better diagnosis results for composite faults, and the accuracy of fault classification can reach 100%, which provides a new way to solve the problem of composite fault diagnosis.

# Piecewise analysis in time series
* [Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise](https://www.mdpi.com/1424-8220/22/17/6599)[2022]

Author: Lei Hu, Ligui Wang

Key word: rolling bearings, fault diagnosis, piecewise aggregate approximation, CEEMDAN, __Piecewise__.

Abstract: Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low computational efficiency. In each iteration of CEEMDAN, fault vibration signals are added with noises, both the vibration signals added with noises and the added noises are decomposed with classical empirical mode decomposition (EMD). This paper proposes a rolling bearing fault diagnosis method that combines piecewise aggregate approximation (PAA) with CEEMDAN. PAA enables CEEMDAN to decompose long signals and to achieve enhanced diagnosis. In particular, the method first yields the vibration envelope using bandpass filtering and demodulation, then compresses the envelope using PAA, and finally decomposes the compressed signal with CEEMDAN. Test data verification results show that the proposed method is more effective and more efficient than CEEMDAN.

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* [An intelligent fault diagnosis framework based on piecewise aggregate approximation, statistical moments, and sparse autoencoder](https://journals.sagepub.com/doi/epub/10.1177/1748006X221108598)[2022]

Author: Akash Prasad, Chirag Dantreliya

Key word: Rotating machinery, fault diagnosis

Abstract:　Rotating machines (RMs) have vast applicability in almost all the industries in mechanical domain. Rolling element bearings (RBs) are the key elements to ensure that the RMs perform efficiently. RBs are highly prone to wear and tear which could have devastating consequences such as massive economic losses and accidents. In the past, many time-domain based condition-indicators such as root mean square (RMS), skewness and kurtosis, etc. have been proposed by researchers to diagnose the bearing faults and prevent RM failures. However, they are often insensitive to early stage faults, affected by outliers and possess poor degradation tracking characteristics. To overcome these shortcomings, this paper proposes a novel statistical feature extraction technique called as multiscale statistical moment (MSM) analysis, in combination with sparse autoencoder to detect the incipient faults as well as track the progression of wear. Firstly, the vibration signal are acquired from the bearings to be monitored. Secondly, the MSM features are extracted from the vibration signals. Thirdly, the MSM features corresponding to normal conditions are utilized to train the sparse autoencoder network. Fourthly, the MSM features corresponding to test conditions are supplied to the pre-trained sparse autoencoder model. The MSM technique offers the advantage that it extracts the fault properties contained in multiple time-scales of the vibration signals instead of a single time-scale only. Finally, the dissimilarity between the actual and predicted output is measured to obtain the bearing health indicator (BHI). The experimental results demonstrate that the suggested BHI detects the faults at early stages, possess better sensitivity and trends the bearing degradation more accurately as compared to the traditional techniques such as RMS, kurtosis, and BHI obtained with statistical moment features at single-scale only.

# Piecewise + SS like - systemetic sempling
* [A Novel Feature Extraction Approach for Mechanical Fault Diagnosis Based on ESAX and BoW Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9805609)[2022]

Author: Dongfang Zhao , Shulin Liu

Key word: Bag-of-words (BoW), fault diagnosis, feature
extraction, Laplacian score (LS), symbolic aggregate approximation (SAX) __Piecewise__, __Systemetic sampling__.

Abstract: Condition monitoring and fault diagnosis are of great significance to the development of modern industry, for they enable enterprises to avoid unexpected interruptions or severe accidents, and extracting the fault-related features from vibration signals is a critical step to achieve accurate diagnosis. Among diverse of feature extraction approaches, symbolic aggregate approximation (SAX) is a promising one that has been introduced into fault diagnosis recently. Nevertheless, when dealing with the sampled vibration signals, the SAX ignores the change of signal frequency characteristics, which eventually leads to information aliasing and cannot ensure the information validity. In this work, the information aliasing is analyzed from the perspective of signal processing, and the extremum symbolic aggregate approximation (ESAX) is developed as a substitution on the premise of maintaining the validity of the information. Subsequently, to convert the symbol strings generated by the ESAX into usable digital feature vectors, the bag-of-words (BoW) model in natural language processing (NLP) is employed to perform the counting statistics of the fault-related words, and the Laplacian score (LS) algorithm is then utilized to rerank the statistical results, thereby realizing the extraction of mechanical fault feature. The superiority of the developed method is verified by experiments.