Fault diagnosis of an automobile cylinder head using low frequency vibrational data

作者：Taajobian Maryam; Mohammadzaheri Morteza^*; Doustmohammadi Mojtaba; Amouzadeh Amirhosein; Emadi Mohammadreza

来源：Journal of Mechanical Science and Technology, 2018, 32(7): 3037-3045.

DOI：10.1007/s12206-018-0606-x

• Artificial neural network
• Automobile
• Fault diagnosis
• Fault signature
• Vibrations

This paper proposes a vibration-based fault-diagnosis method for mechanical parts. This method, after algorithm development, only requires a single inexpensive test to inspect the part which could take as short as half a second. The algorithm is developed in three major stages, (i) exciting specimens without or with known faults using a controlled force and recording acceleration of a single point for a short time (ii) finding a signature for each faulty specimen, using Fourier transform and statistical analysis. (iii) Developing a multi-layer perceptron, as a mathematical model, using the results of stage (ii). The elements of a part signature are the inputs to the model. The location (and possibly size and shape factor) of the fault is model output. Stage (i) can be performed experimentally or alternatively with a validated FEM, one experiment or simulation per specimen. The proposed technique was examined to locate (isolate) a fault on an automobile cylinder head. The presented accuracy is considerable, and the data collected at fairly low frequency range (below 1200 Hz) were found to be sufficient for this technique. In the case study of this paper, possible fault locations are on a line; as a result, fault location has one dimension. It is shown that the technique can be extended to higher dimensions.

• 出版日期2018-7