Cutting tools are required for day to day activities in manufacturing. Continuous machining operations lead tool to undergo wear. Worn out tools effect surface finish during machining. The dimensional accuracy of components is also compromised. Robust tool health is vital for better productivity. Hence, an online system condition monitoring of tools is the need of hour, promising reduction in maintenance cost with a greater productivity saving both time and money. This paper presents the classification performance of K-star algorithm. A set of statistical features extracted from vibration signals (good and faulty conditions) form the input to algorithm. In the present study, the K-star algorithm is able to achieve 78% classification accuracy. © 2013 Elsevier Ltd. All rights reserved.

Sugumaran V

Mechanical

School of Mechanical Engineering

Chennai Campus

Painuli S

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Expert Systems with Applications

In this study, the application of wavelets has been investigated for diagnosing the faults on a hydraulic brake system of a light motor vehicle using the vibration signals acquired from a brake test setup through a piezoelectric type accelerometer. An efficient brake system should provide reliable and effective performance in order to ensure safety . If it is not properly monitored, it may lead to a serious catastrophic effect such as accidents, frequent breakdown, etc. Hence, the brake system needs to be monitored continuously. The condition of the brake components and the vibration signals are interrelated. If the failure starts progressing, the vibration magnitude will also progress. Analyzing the vibration signals under the various fault conditions is the key process in fault diagnosis. In recent decades wavelets have been focused on in many fault diagnosis studies as the wavelets decompose the complex information into simple form with high precision for further analysis. The wavelet features were extracted in order to retrieve the information from the vibration signals using discrete wavelet transform. From that discretized signal under each fault condition, the relevant features were extracted and feature selection was carried out. The selected features were then classified using a set of machine learning classifiers such as best first tree (pre-pruning, post-pruning, and unpruned), Hoeffding tree (HT), support vector machine, and neural network. The classification accuracies of all the algorithms were compared and discussed. Among the considered classifier model, the HT model produced a better classification accuracy as 99.45% for the hydraulic brake fault diagnosis.

Journal of Vibration and Control

Vibration based brake health monitoring using wavelet features: A machine learning approach

Drilling is a frequently used machining operation in manufacturing industries. Continuous operation of the drilling tool results in tool wear which affects the surface finish and dimensional accuracy of the work. In this paper, the feasibility of early detection of limiting tool wear has been explored. Tool performance is monitored by acquiring the vibration signals developed during the drilling of stainless steel plates using HSS tool. Statistical features are extracted from the vibration signals based on which the tool wear condition is differentiated by K-Star classifier. The classification accuracy obtained is 79.56%. © 2019 Society of Manufacturing Engineers (SME)

Manufacturing Letters

Early detection of drilling tool wear by vibration data acquisition and classification

Gearbox is the most important component in automobiles. Gearbox can lead to serious damage if failure in any components occur and even lead to accidents. Thus the gearbox plays a vital role in a vehicle and hence condition monitoring is essential. Gearbox components state can be examined by having the vibration attributes which tells the gearbox state and condition. The main objective of this study is to propose a combinational classifier (Hybrid Classifier) for identifying and diagnosing faults in the gearbox using vibration as a solution to the problem. The vibration signals of the gearbox are acquired. Vibration Signals are used to extract the features by using the methods of feature extraction and selection of features are carried out before these variables are given as input to the classifier. Since no such procedures are obtainable to find productive number of attributes a detailed research is required to identify the best viable number of characteristics for given problem. The selected features are then classified using the proposed classification algorithm. The proposed classification accuracy of the technique has been compared with other machine learning approaches and discussed. The proposed fault diagnosis model can estimate the state and condition of process at any stage and prevents from unpredicted Failures. © 2017 IEEE.

2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

Vibration based expert system: Review

Fulltext

Indian Journal of Science and Technology

Vibration Based Condition Monitoring of a Hydraulic Brake System through Statistical Learning Approaches: A Review

Fault Diagnosis of a Single Point Cutting Tool using Statistical Features by Random Forest Classifier

Machining Science and Technology

TOOL CONDITION MONITORING USING ACOUSTIC EMISSION, SURFACE ROUGHNESS AND GROWING CELL STRUCTURES NEURAL NETWORK

Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm

International Journal of Machine Tools and Manufacture

Development and evaluation of an on-machine optical measurement device

The International Journal of Advanced Manufacturing Technology

Acoustic emission method for tool condition monitoring based on wavelet analysis

Tool wear monitoring system for CNC end milling using a hybrid approach to cutting force regulation

Tool condition monitoring using K-star algorithm

Journal	Data powered by TypesetExpert Systems with Applications
Publisher	Data powered by TypesetElsevier BV
ISSN	0957-4174
Open Access	No