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Machine Diagnostics
Our team of inspection engineers is one of the most experienced and well educated. We have done many inspections and Failure root cause analyses in many industrial, maritime and wind industries. Further our department has knowledge on:
-Condition based maintenance
-Equipment health management
-Torque and power measurements
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.
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.
The most preferred method of remote monitoring is Condition Based Monitoring (CBM) in manufacturing industries. Condition monitoring is a non-destructive and non-disruptive technique of monitoring plant equipment to determine its health or condition at any point in time. Remote condition monitoring determines the stability or possible deterioration of equipment condition. The rate of deterioration and where exactly is the equipment in its life cycle are also determined and conveyed to concerned stakeholders.
Commonly used condition monitoring techniques in manufacturing industries are:
Vibration Analysis: Measuring vibrations within a component, machinery, or machine group.
Oil Analysis: Analysing oil contamination, and machine wear to determine asset health.
Electrical Analysis: Analysing incoming power quality from the equipment, using motor current readings.
Ultrasonic Analysis: Detecting high-frequency sound waves through ultrasonic analysis.
Infrared Thermography: Detecting radiations and temperature changes using a thermal imager.
In sum, machine diagnostics and remote monitoring are instrumental in managing asset performance and improving overall plant reliability. Real-time machine diagnostics using sophisticated, IoT-enabled techniques can give maintenance teams better visibility of asset conditions. With remote monitoring, machine diagnostics can be performed in a cost-efficient and secure manner, giving improved data accessibility across the organization. As machine faults are predictively diagnosed, corrective actions can be taken before the incidence of actual machine breakdown.
-Condition based maintenance
-Equipment health management
-Torque and power measurements
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.
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.
The most preferred method of remote monitoring is Condition Based Monitoring (CBM) in manufacturing industries. Condition monitoring is a non-destructive and non-disruptive technique of monitoring plant equipment to determine its health or condition at any point in time. Remote condition monitoring determines the stability or possible deterioration of equipment condition. The rate of deterioration and where exactly is the equipment in its life cycle are also determined and conveyed to concerned stakeholders.
Commonly used condition monitoring techniques in manufacturing industries are:
Vibration Analysis: Measuring vibrations within a component, machinery, or machine group.
Oil Analysis: Analysing oil contamination, and machine wear to determine asset health.
Electrical Analysis: Analysing incoming power quality from the equipment, using motor current readings.
Ultrasonic Analysis: Detecting high-frequency sound waves through ultrasonic analysis.
Infrared Thermography: Detecting radiations and temperature changes using a thermal imager.
In sum, machine diagnostics and remote monitoring are instrumental in managing asset performance and improving overall plant reliability. Real-time machine diagnostics using sophisticated, IoT-enabled techniques can give maintenance teams better visibility of asset conditions. With remote monitoring, machine diagnostics can be performed in a cost-efficient and secure manner, giving improved data accessibility across the organization. As machine faults are predictively diagnosed, corrective actions can be taken before the incidence of actual machine breakdown.