Hydraulic brakes are the most important components in automobile. It requires advanced supervision and fault diagnosis to improve the safety of passenger, reliability and economy. Condition monitoring is one of the major division through which the reliability of such components could be monitored. The condition of the brake components can be monitored by using the vibration characteristics which will reveal the condition of the brake systems. In this paper machine learning algorithm using vibration monitoring is proposed as a possible solution to this problem. From the hydraulic brake test set up, the vibration signals were acquired by using a piezoelectric transducer and data acquisition system. C4.5 decision tree algorithm was used to extract statistical features from vibration signals. Feature selection was also carried out. Since no much of methodologies are available to find the effective number of features for a given problem, a detailed study is needed to find the best possible number of features. Hence the effect of number of features was studied by using decision tree. The selected features were classified using C4.5 decision tree algorithm and Best first decision tree algorithm with pre pruning and post pruning techniques. The results are discussed and conclusions of the study are presented. © 2013 Elsevier Ltd. All rights reserved.

Jegadeeshwaran R

School of Mechanical Engineering

Chennai Campus

Sugumaran V

Mechanical

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

Measurement

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

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

Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade. © 2017 ISA

ISA Transactions

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study

Fulltext

Indian Journal of Science and Technology

Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithm

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

Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers. © 2011 Elsevier B.V. All rights reserved.

Applied Soft Computing

Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump

Classification And Regression Trees

Background

A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis

Expert Systems with Applications

Effect of number of features on classification of roller bearing faults using SVM and PSVM

International Journal of Granular Computing, Rough Sets and Intelligent Systems

Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump

Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features

Journal	Data powered by TypesetMeasurement
Publisher	Data powered by TypesetElsevier BV
ISSN	0263-2241
Open Access	No