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Fault detection signal processing notes

Fault detection signal processing notes

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Conditional Monitoring and Diagnostics

(Code: 06-88-590-86)

Group Members Details: -

Sr. No.

Name

Student ID

ALEESHA SUSAN JACOB

104927373

ILLAKIYA RAJA DURAIPANDI

104916854

OGIEVAMWEN ONI

103245780

Project Name: Fault Detection – Signal Processing

By Fast Fourier Transform Method, Code: C2

Primary Report

Guided by: Prof. Roozbeh Razavi-Far

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ABSTRACT:

Modern machineries are expensive, the demand for higher system reliability with less downtime

and the desire to shorten the mean-time between failures, high availability and effectiveness is

forcing equipment vendors to incorporate condition-monitoring systems into their equipment

designs.

Bearing is a machine element that have constrained relative motions and its function is to reduce

friction between moving parts of a machine to obtain desired motion and support radial load and

axial loads. Bearings are among the most fundamental elements in rotating machinery and

constitutes major failures in the machine. Bearings require an effective conditional monitoring to

avoid machine unplanned downtime by accurate, rapid and automatic fault detection techniques

using linear acceleration or acoustic emission signals, force sensors etc. Based on its functioning,

bearings are critical components. Any noise or vibration depicts fault in them. Poor fault detection

causes malfunction and damage to the vehicle. Normally faults, each one with varying frequency

occur in rolling element, inner race or outer race of the bearing. Fault in the bearing can be because

of spatially constrained contact between the rollers and races combined with the time-varying

nature of bearing loading which drives fatigue damage. These faults arise on the inner/outer races

or roller elements when imperfections on the material surface propagate to form cracks.

This report presents the vibro-acoustic Fault analysis of ball bearing using the signal processing

technique called Fast Fourier Transform (FFT) we have other techniques that can be used such as

EMD (Empirical Mode Decomposition), EEMD (Ensemble EMD), CEEMDAN (Complete

EEMD with Adaptive Noise.

Introduction

Condition Based Monitoring (CBM) is a type of predictive maintenance that involves using device

to measure the status of a machine component over time while it is in operation to determine when

failure will likely occur.The automated industry highly relies on healthy machinery which is

analyzed based on the signal generated by a ball bearing. In process control, telecommunication,

and automated industries, human perception was key to analyze the nature of the signal but fails

to deliver the meticulous character of the signal generated by the equipment. The advancement in

technology leads to overcoming the human error by using sensors and signal processing techniques

such as Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform

and Hilbert Huang Transform (HHT). Industrial equipment generates two types of signals namely-

acoustic and vibration signals. By Empirical Mode Decomposition (EMD) process these signals

are deconstructed into a set of intrinsic mode functions (IMF) from which frequency is extracted.

Fast Fourier Transform

Through Fast-Fourier Transform, time domain signals are converted to frequency domain signals.

It computes Discrete Fourier Transform at twice the speed (from complex matrix to sparse matrix)

Its complexity is O (nlogn) where n is the data size unlike DFT which has the complexity O (n

)

depicting a faster rate to solving a problem. Below diagram shows the FFT block diagram.

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Conditional Monitoring and Diagnostics (Code: 06-88-590-86) Group Members Details: Sr. No. Name Student ID 1. ALEESHA SUSAN JACOB 104927373 2. ILLAKIYA RAJA DURAIPANDI 104916854 3. OGIEVAMWEN ONI 103245780 Project Name: Fault Detection – Signal Processing By Fast Fourier Transform Method, Code: C2 Primary Report Guided by: Prof. Roozbeh Razavi-Far ABSTRACT: Modern machineries are expensive, the demand for higher system reliability with less downtime and the desire to shorten the mean-time between failures, high availability and effectiveness is forcing equipment vendors to incorporate condition-monitoring systems into their equipment designs. Bearing is a machine element that have constrained relative motions and its function is to reduce friction between moving parts of a machine to obtain desired motion and support radial load and axial loads. Bearings are among the most fundamental elements in rotating machinery and constitutes major failures in the machine. Bearings require an effective conditional monitoring to avoid machine unplanned downtime by accurate, rapid and automatic fault detection techniques using linear acceleration or acoustic emission signals, force sensors etc. Based on its functioning, bearings are critical components. Any noise or vibration depicts fault in them. Poor fault detection causes malfunction and damage to the vehicle. Normally faults, each one with varying frequency occur in rolling element, inner race or outer race of the bea ...
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