Condition monitoring of an electro-magnetic brake using an artificial neural network

4th International Conference on Mechanical Engineering Research

Gofran, T.^1 ; Neugebauer, P.^1 ; Schramm, D.^2

IEEM - Institute for Energy Efficient Mobility, Karlsruhe Applied Science University, Germany^1

University of Duisburg-Essen, Germany^2

关键词: Data-driven approach;    Electrical data;    Electromagnetic brakes;    Existing systems;    Feed-forward artificial neural networks;    Friction surfaces;    Indirect sensing;    Supervised learning methods;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/257/1/012050/pdf
DOI  :  10.1088/1757-899X/257/1/012050

来源: IOP

【 摘 要 】

This paper presents a data-driven approach to Condition Monitoring of Electromagnetic brakes without use of additional sensors. For safe and efficient operation of electric motor a regular evaluation and replacement of the friction surface of the brake is required. One such evaluation method consists of direct or indirect sensing of the air-gap between pressure plate and magnet. A larger gap is generally indicative of worn surface(s). Traditionally this has been accomplished by the use of additional sensors - making existing systems complex, cost- sensitive and difficult to maintain. In this work a feed-forward Artificial Neural Network (ANN) is learned with the electrical data of the brake by supervised learning method to estimate the air-gap. The ANN model is optimized on the training set and validated using the test set. The experimental results of estimated air-gap with accuracy of over 95% demonstrate the validity of the proposed approach.