Brake Health Prediction Using LogitBoost Classifier Through Vibration Signals

Brake is one of the crucial elements in automobiles. If there is any malfunction in the brake system, it will adversely affect the entire system. This leads to tribulation on vehicle and passenger safety. Therefore the brake system has a major role to do in automobiles and hence it is necessary to monitor its functioning. In recent trends, vibration-based condition monitoring techniques are preferred for most condition monitoring systems. In the present study, the performance of various fault diagnosis models is tested for observing brake health. A real vehicle brake system was used for the experiments. A piezoelectric accelerometer is used to obtain the signals of vibration under various faulty cases of the brake system as well as good condition. Statistical parameters were extracted from the vibration signals and the suitable features are identified using the effect of the study of the combined features. Various versions of machine learning models are used for the feature classification study. The classification accuracy of such algorithms has been reported and discussed.

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Condition Indicators for Gearbox Condition Monitoring Systems

Acta Polytechnica ◽

10.14311/782 ◽

2005 ◽

Vol 45 (6) ◽

Cited By ~ 11

Author(s):

P. Večeř ◽

M. Kreidl ◽

R. Šmíd

Keyword(s):

Condition Monitoring ◽

Manual Transmission ◽

Monitoring Systems ◽

Vibration Signals ◽

Vibration Diagnostics ◽

Helical Gears ◽

Condition Indicators ◽

Condition Monitoring Systems ◽

Durability Testing ◽

Condition Indicator

Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs) are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears.

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Remote data acquisition for condition monitoring of wind turbines

International Journal Sustainable Construction & Design ◽

10.21825/scad.v6i1.1138 ◽

2015 ◽

Vol 6 (2) ◽

pp. 10

Author(s):

Bavo De Maré ◽

Jacob Sukumaran ◽

Mia Loccufier ◽

Patrick De Baets

Keyword(s):

Data Acquisition ◽

Wind Turbine ◽

Condition Monitoring ◽

Wind Turbines ◽

Good Condition ◽

Offshore Wind ◽

Monitoring Systems ◽

Offshore Wind Turbines ◽

Condition Monitoring Systems ◽

Remote Data

While the number of offshore wind turbines is growing and turbines getting bigger and more expensive, the need for good condition monitoring systems is rising. From the research it is clear that failures of the gearbox, and in particular the gearwheels and bearings of the gearbox, have been responsible for the most downtime of a wind turbine. Gearwheels and bearings are being simulated in a multi-sensor environment to observe the wear on the surface.

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Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier – A Machine Learning approach

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2015.v6i1.2242 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

R. Jegadeeshwaran ◽

V. Sugumaran

Keyword(s):

Machine Learning ◽

Condition Monitoring ◽

Forest Tree ◽

Brake System ◽

Learning Approaches ◽

Statistical Features ◽

Vibration Signals ◽

Hydraulic Brake ◽

Random Forest Tree ◽

On The Road

Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraulic brake test setup using a piezoelectric accelerometer and a data acquisition system. Condition monitoring of brakes was studied using machine learning approaches. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. Feature classification was carried out using nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. A Random forest tree algorithm was used as a base classifier for the nested dichotomy (ND) classifiers. The effectiveness of the suggested techniques was studied and compared. Amongst them, class balanced nested dichotomy (CBND) with the statistical features gives better accuracy of 98.91% for the problem concerned.

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