Managing machine health and ensuring sustainable asset performance is an important control objective in manufacturing environments. In both discreet and process-based manufacturing industries, net plant productivity, operational costs, and return on assets (ROA) depend on how well the available assets are utilized. This makes monitoring and diagnosing faults in all available assets a high priority for maintenance teams.
This article will cover what machine diagnostics are and how remote monitoring enables maintenance teams to track machine conditions, detect existing or impending faults, isolate the root cause, and initiate procedures to restore machine health.
What is Machine Diagnostic?
Machine diagnostics is an equipment control measure that focuses on tracking machine conditions and detecting faults that can lead to machine breakdown. The process includes fault detection, followed by root cause analysis. Machine diagnostics is critical in rotating machinery that operates round-the-clock and is essential for undisrupted production in a manufacturing environment.
Diagnostic data is collected with the help of various offline and online techniques, depending upon the scope of the maintenance. This data is then analyzed using methods like high-resolution spectral analysis, wavelet analysis or transform, Fast Fourier Transform (FFT), Wigner-Ville Distribution, waveform analysis, etc. The analyzed data is used to determine the root cause of faults and recommend corrective actions to the maintenance and operation teams.
For instance, a boom conveyor in a steel manufacturing plant can break down due to faults in motors, pulleys, or gearboxes. In a complex machine like this, it is difficult to determine what could have caused the breakdown and what kind of maintenance activity is required. With machine diagnostics, tri-axial vibration trends of the boom conveyor can be monitored to determine the root cause of machine breakdown.
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