Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal

Madhusudana, C K; Kumar, Hemantha; Narendranath, S.

doi:10.1016/j.matpr.2018.02.178

Cited by 41 publications

(26 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A standard tree is represented by the J48 algorithm, which consists of a root node, a number of leaf nodes, and a number of branches. Each branch of a tree represents a chain of nodes from the root to a leaf, and each node represents an attribute (or feature) [15][16][17] . Decision trees are one of the most effective and widely used techniques in many areas of Data Mining, such as pattern recognition, machine learning, image processing and information retrieval [18][19][20] .…”

Section: Recognition Using Decision Treementioning

confidence: 99%

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Liao

Chen

Guan

et al. 2021

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

China has the world's largest planting area of paddy rice, but large quantities of paddy rice fall to the ground and are lost during harvesting with a combine harvester. Reducing grain loss is an effective way to increase production and revenue. In this study, a monitoring system was developed to monitor the grain loss of the paddy rice and this approach was tested on the test bench for verifying the precision. The development of the monitoring system for grain loss included two stages: the first stage was to collect impact signals using a piezoelectric film, extract the four features of Root Mean Square, Peak number, Frequency and Amplitude (fundamental component), and identify the kernel impact signals using the J48 (C4.5) Decision Tree algorithm. In the second stage, the precision of the monitoring system was tested for the paddy rice at three different moisture contents (10.4%, 19.6%, and 30.4%) and five different grain/impurity ratios (1/0.5, 1/1, 1/1.5, 1/2, and 1/2.5). According to the results, the highest monitoring accuracy was 99.3% (moisture content 30.8% and grain/impurity ratio 1/2.5), the average accuracy of the monitoring tests was 92.6%, and monitoring of grain/impurity ratios between 1/1 and 1/1.5 (>95.4%) had higher accuracy than monitoring the other grain/impurity ratios. Monitoring accuracy decreased as impurities increased. The lowest accuracy for grain loss monitoring was obtained when the grain/impurity ratio was 1/2.5, with monitoring accuracies of 88.2%, 75.7% and 78.8% at moisture contents of 10.4%, 19.6% and 30.4%.

show abstract

Section: Recognition Using Decision Treementioning

confidence: 99%

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Liao

Chen

Guan

et al. 2021

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

show abstract

“…Several parameters are chosen by the ANN classifier including the number of training samples, the number of hidden nodes, and the learning rate for classification performance improvement. However, the performance is not priori possible as with other non-parametric approaches to pattern classification [44]. The performance of ANN is determined by the weight updation, propagation function and the learning rule.…”

Section: Artificial Neural Network Classifier (Ann)mentioning

confidence: 99%

Research on fan vibration fault diagnosis based on image recognition

Huang¹,

Qiao²,

Khanna³

et al. 2021

J. vibroeng.

View full text Add to dashboard Cite

The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this paper, a novel image recognition technique is proposed for vibration-based fault diagnosis using the spectrum images of the vibration signals. 1D vibration signal spectrum is initially computed using Fast Fourier Transform (FFT) and the FFT frequencies are adjusted such that it captures a vibration spectrum diagram as 2D image representation. FFT based vibration analysis is done and the image recognition concept is utilized for feature extraction and a machine learning classification module is used for fault analysis and diagnosis. Effective feature generation is done using Principal Component Analysis (PCA) by removing the redundancy from the feature vectors and machine learning classifiers are used to obtain improved image recognition and classification performance. Artificial Neural Network (ANN) classifier yields better performance in terms of various performance parameters and percentage improvement in terms of accuracy for ANN classification methods over Support Vector Machine (SVM), k-Nearest Neighbours (kNN) and Random Forest Ensemble (RFE) methods are 10.01 %, 4.51 % and 2.01 % respectively. Comparative scenarios are considered in this work for fan vibration fault detection as well as diagnosis based on the image features for various realistic vibration fault conditions. Effectiveness of the proposed image recognition-based technique is compared with the state-of-the-art methods, justifying its outperformance for fan fault detection and diagnosis using the combination of spectrum adjustment, PCA and ANN classification method.

show abstract

“…Liangyu et al [113] propose an artificial NN combined with optimal zoom search to recognize various degrees of failure in a high-pressure feedwater heater system. The classification of the healthy and defective conditions of a face milling tool is done through the acquisition of sound signals using the discrete WT (DWT) and the J48 algorithm, which is a decision tree technique [114]. On the other hand, to detect and diagnose faults in HVAC systems, Du et al [115] combine NNs and clustering analysis.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

View full text Add to dashboard Cite

Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

show abstract

Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal

Cited by 41 publications

References 17 publications

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

Research on fan vibration fault diagnosis based on image recognition

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Contact Info

Product

Resources

About