Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

Shantisagar, Kundur; Jegadeeshwaran, R.; Gandhi, Sakthivel; Manghai, T. M. Alamelu

doi:10.32604/sdhm.2019.00355

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Hence, the overall classification accuracy is 96.91% (533 data points out of 550). This classification accuracy with histogram is more than that of the classification accuracy with the statistical features (95.09%) [26]. However, misclassification is only 3.09%, which is good enough for many practical applications.…”

Section: Fuzzy Inference Enginementioning

confidence: 81%

Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

Marimuthu¹,

Jegadeeshwaran²,

Jakkamputi³

et al. 2022

Structural Durability &Amp; Health Monitoring

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The requirement of fault diagnosis in the field of automobiles is growing higher day by day. The reliability of human resources for the fault diagnosis is uncertain. Brakes are one of the major critical components in automobiles that require closer and active observation. This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis. Vibration signals of a rotating element contain dynamic information about its health condition. Hence, the vibration signals were used for the brake fault diagnosis study. The study was carried out on a brake fault diagnosis experimental setup. The vibration signals under different fault conditions were acquired from the setup using an accelerometer. The condition monitoring of the hydraulic brake system using the vibration signal was processed using a machine learning approach. The machine learning approach has three phases, namely, feature extraction, feature selection, and feature classification. Histogram features were extracted from the vibration signals. The prominent features were selected using the decision tree. The selected features were classified using a fuzzy classifier. The histogram features and the fuzzy classifier combination produced maximum classification accuracy than that of the statistical features.

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Section: Fuzzy Inference Enginementioning

confidence: 81%

Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

Marimuthu¹,

Jegadeeshwaran²,

Jakkamputi³

et al. 2022

Structural Durability &Amp; Health Monitoring

View full text Add to dashboard Cite

show abstract

“…There are 8 articles about FIS: [33,76,[83][84][85][86][87][88] and 18 articles for the neuro-fuzzy approach: [33,36,37,41,58,[89][90][91][92][93][94][95][96][97][98][99][100][101]. For the inference system, it is important to note that, out of the 8 articles, five articles have the author Balazinski Marek (and Baron Luc for four of these articles) in the author list which explains the similarity in the approaches presented in these articles ( [33,84,[86][87][88]).…”

Section: Presentation Of the Articlesmentioning

confidence: 99%

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

et al. 2021

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In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

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“…Methods combining statistical features and threshold judgment have been widely used from the early days to the present. With the development of artificial intelligence technology, machine learning algorithms such as k-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), and artificial neural network (ANN) are combined with the statistical characteristics of signals in the time domain or frequency domain and applied in vibration-based fault diagnosis [19][20][21]. Compared with threshold judgment methods, machine learning methods do not need to conduct in-depth research on the vibration mechanism of equipment faults, nor do they need to manually design the judgment threshold, which greatly reduces the difficulty for engineering applications to enact fault diagnosis methods [22,23].…”

Section: Introductionmentioning

confidence: 99%

Research on Vibration Data-Driven Fault Diagnosis for Iron Core Looseness of Saturable Reactor in UHVDC Thyristor Valve Based on CVAE-GAN and Multimodal Feature Integrated CNN

et al. 2022

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The imbalance of data samples and fluctuating operating conditions are the two main challenges faced by vibration data-driven fault diagnosis for the iron core looseness of saturable reactors in UHVDC thyristor valves. This paper proposes a vibration data-driven saturable reactor iron core looseness fault diagnosis strategy named CVG-MFICNN based on CVAE-GAN and MFICNN to overcome the two challenges. This strategy uses a novel 1-D CVAE-GAN model to produce generated samples and expand the training set based on imbalanced training samples. An MFICNN model structure is designed to allow the simultaneous processing of multimodal features such as the SST time-frequency spectrum, time-domain vibration sequence, frequency-domain power spectrum sequence, and time-domain statistics. Using these multimodal features and the MFICNN model, the hidden fault information in vibration data can be effectively mined. An experiment is conducted to collect vibration data of saturable reactors with different faults. Models based on the proposed strategy and other methods are trained and tested using the collected data. The comparison results show that the performance of the proposed CVG-MFICNN approach is significantly superior to that of single-feature CNNs, traditional machine learning methods, and classical image classification CNNs in the application of UHVDC thyristor valve saturable reactor iron core looseness fault diagnosis.

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Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

Cited by 8 publications

References 24 publications

Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

Research on Vibration Data-Driven Fault Diagnosis for Iron Core Looseness of Saturable Reactor in UHVDC Thyristor Valve Based on CVAE-GAN and Multimodal Feature Integrated CNN

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