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

Jegadeeshwaran, R.; Sugumaran, V.

doi:10.1016/j.measurement.2013.04.068

Cited by 58 publications

(27 citation statements)

References 18 publications

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“…• Drive shaft speed: 60 km / Hr The most frequently occurring faults were simulated one at a time while other components remained in good condition, and the corresponding vibration signals were acquired [9]. The simulated faults are as follows: Air in the brake fluid (AIR), Oil spill on the disc brake (BOS), Drum brake pad wear (DRPW) (Figure 3 (a)), Inner brake pad wear -uneven (UDPWI), Uneven brake pad wear -inner and outer (UDPWIO), inner brake pad wear -even (DPWI), Even brake pad wear -inner and outer (DPWIO), (Figure 3 Figure 4 shows the schematic of the data acquisition process.…”

Section: Experimental Setup and Experimental Proceduresmentioning

confidence: 99%

“…The feature classification process is the process of classifying data into categories. Many classifiers, such as Support Vector Machines [2,6,10], Adapted neuro-fuzzy inference system (ANFIS) [6], decision tree and best first tree [9], have been reported for the fault diagnosis of various applications. To enhance the characterization precision a point by point study is required.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vibration based Condition Monitoring of a Brake System using Statistical Features with Logit Boost and Simple Logistic Algorithm

Mangai.

Jegadeeshwaran²,

Sugumaran³

2018

IJPE

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Brakes are responsible for the stability of the vehicle. Brake failure is one of the key elements where more attention is required. Normally, a brake system failure is not an instantaneous process. It is caused by faults due to reasons like wear, mechanical fade, and oil leak, which started long before the failure progresses. Hence, it is essential to build a model that can recognize the condition from the signal. Condition monitoring is one such supervision approach, which continuously monitors the system and gives characteristics data. These data can be analysed and the condition of the component can be extracted using a machine learning approach. This study focuses on one such machine learning approach using the vibration characteristics of the brake system. The machine learning approach was carried out using feature extraction and feature classification. The statistical information extracted from the vibration signals under various fault conditions were used as features. The features were classified using machine learning algorithms, namely, Simple logistics, Logit boost and Multinominal Regression. Results were compared and discussed. The Logit boost algorithm, which produced 98.91 % classification accuracy, has been suggested as an effective approach for the brake fault diagnosis study.

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Section: Experimental Setup and Experimental Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vibration based Condition Monitoring of a Brake System using Statistical Features with Logit Boost and Simple Logistic Algorithm

Mangai.

Jegadeeshwaran²,

Sugumaran³

2018

IJPE

View full text Add to dashboard Cite

show abstract

“…(Original Speed 667 rpm, Brake load 67.7 N). From the accelerometer, the vibration signals for different fault conditions were taken with the following settings [17].…”

Section: Experimental Studiesmentioning

confidence: 99%

“…They are mean, standard error, sample variance, kurtosis, skewness, minimum, maximum, standard deviation, count, and mode and median. The definition of the extracted statistical features was described in a fault diagnosis approach [17]. Following the study performed by Sugumaran et al, these twelve features were extracted from vibration signals using Visual basic tool [18].…”

Section: Feature Extractionmentioning

confidence: 99%

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Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) – A statistical learning approach

Jegadeeshwaran

Sugumaran

2015

Engineering Science and Technology, an International Journal

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a b s t r a c tIn automobile, brake system is an essential part responsible for control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the vehicle cum passenger's safety. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is essential. Vibration based condition monitoring using machine learning techniques are gaining momentum. This study is one such attempt to perform the condition monitoring of a hydraulic brake system through vibration analysis. In this research, the performance of a Clonal Selection Classification Algorithm (CSCA) for brake fault diagnosis has been reported. A hydraulic brake system test rig was fabricated. Under good and faulty conditions of a brake system, the vibration signals were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signal. The best feature set was identified for classification using attribute evaluator. The selected features were then classified using CSCA. The classification accuracy of such artificial intelligence technique has been compared with other machine learning approaches and discussed. The Clonal Selection Classification Algorithm performs better and gives the maximum classification accuracy (96%) for the fault diagnosis of a hydraulic brake system.

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Decision Trees and Random Forests

2019

Condition Monitoring With Vibration Signals

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

Cited by 58 publications

References 18 publications

Vibration based Condition Monitoring of a Brake System using Statistical Features with Logit Boost and Simple Logistic Algorithm

Vibration based Condition Monitoring of a Brake System using Statistical Features with Logit Boost and Simple Logistic Algorithm

Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) – A statistical learning approach

Decision Trees and Random Forests

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