RetComm 1.0: Real Time Condition Monitoring of Rotating Machinery Failure

Lee Chun Hong,Sharifah Saon,Abd Kadir Mahamad,S.A Aljunid,M.A.A Mohd Salleh,P.J Soh,K.N.F Ku Azir,C.B.M Rashidi

doi:10.1051/matecconf/201815001002

Lee Chun Hong, Sharifah Saon + Show 6 more

Open Access

https://doi.org/10.1051/matecconf/201815001002

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Abstract

The breakdown of motor proves to be very expensive as it increases downtime on the machines. Development of cost-effective and reliable condition monitoring system for the protection of motors to avoid unexpected breakdowns is necessary. Therefore, RetComm 1.0 is developed as assistant tool for bearing condition diagnosis system. The smartphone accelerometer is used to collect the vibration signal data and send it to computer by using the Android application named Matlab Mobile. The Matlab software is used to implement a program which is the RetComm 1.0 system to analyse the vibration signal and monitor the condition of the bearing. The algorithm used to observe the condition of bearing is trained by using Artificial Neural Network (ANN). In this project, the ANN is trained by using Matlab software. This proposed method is implemented for early diagnosis purposes. The diagnosis process can be done by just attached the smartphone onto the bearing for data collection. In conclusion, the bearing condition can be identified with this system. The bearing condition are shown in text to let the user know the bearing conditions. The raw data and power spectrum graph plotting are to let the user more further to understand the health condition of the bearing.

Highlights

The breakdown of motor proves to be very expensive as it increases downtime on the machines
The vibration signal used is the example of the normal condition of bearing
The power spectrum is calculated by applying Fast Fourier Transform (FFT) into the vibration signal data

Summary

Introduction

The breakdown of motor proves to be very expensive as it increases downtime on the machines. It becomes necessary to develop some cost-effective and reliable condition monitoring system for the protection of motors to avoid unexpected breakdowns [1]. Condition monitoring techniques which available in the market are obvious choice for the industries. Prediction provides engineers information scheduling the maintenance [2]. Based on the previous researchers, the condition monitoring methods of the bearing faults are included temperature, acoustic emission, grindings, vibration and oil film resistance [3,4,5,6]. Vibration diagnosis is the most effective method and widely used at this present time

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