Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey
Authors: Saleem Riaz, Hassan Elahi, Kashif Javaid, Tufail Shahzad
Number of views: 284
Safety, reliability, efficiency and performance of rotating machinery in all industrial
applications are the main concerns. Rotating machines are widely used in various industrial applications.
Condition monitoring and fault diagnosis of rotating machinery faults are very important and often
complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and
efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective
bearing fault diagnostic method using different fault features at different steps becomes more attractive.
Bearings are widely used in medical applications, food processing industries, semi-conductor
industries, paper making industries and aircraft components. This paper review has demonstrated that
the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction.
Generally literature is classified into two main groups: frequency domain, time frequency analysis.
However, fault detection and diagnosis of rotating machine vibration signal processing methods to
present their own limitations. In practice, most healthy ingredients faulty vibration signal from
background noise and mechanical vibration signals are buried. This paper also reviews that how the
advanced signal processing methods, empirical mode decomposition and interference cancellation
algorithm has been investigated and developed. The condition for rotating machines based rehabilitation,
prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too.
Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a
key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection
and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the
researchers are widely interested to make automatic procedures for fault extraction techniques. Such
expert systems, neural networks, artificial intelligence and system devices and most powerful methods
described above in conjunction with some of the techniques being used fuzzy inference system