Condition monitoring of low-speed machines, operating at speeds typically below 600 RPM, demands a systematic approach due to the inherent challenges that slow speed operation carries. Here we review some of the techniques utilised for successful monitoring of slow-speed machinery.
The unique characteristics of low-speed machines give rise to a series of challenges when establishing a condition monitoring program:
· Lower frequencies of interest
· Lower impact amplitudes (vibration signals)
· High ambient noise
Lower frequencies of interest:
The slower rotational speeds mean that the fault frequencies of interest in condition monitoring (e.g. bearing ball pass frequency) typically occur at much lower-frequencies and vibrations and dynamic signals, complicating the task of identifying anomalies and irregularities. Lower rotating speeds result in lower amplitudes in the impacts that are often key indicators used in vibration or ultrasound analysis to detect early stage bearing faults. Slow rotating machines are often found in crushing, milling, grinding and other processes, where there are high levels of ambient noise and vibration. The presence of these external noise sources, lowers the signal-to-noise ratio, making it challenging to extract relevant data.
To tackle the complexities of monitoring low-speed machines, a comprehensive approach integrating various complementary methods is preferred. This multi-pronged strategy enhances data accuracy and dependability, facilitating a thorough understanding of the machine’s health. Here are the key techniques commonly employed:
1. Vibration Analysis: Vibration analysis remains a cornerstone technique for low-speed machinery monitoring. Through the utilisation of high sensitivity sensors, longer time waveforms and advanced signal processing algorithms, low level faults associated with operational abnormalities can be detected. Proper sensor selection- and placement, as well as good data collection parameter configuration, are pivotal for optimal data acquisition and analysis.
2. Oil Analysis: Lubricating oil analysis serves as a valuable diagnostic tool for slow-speed machines. By monitoring contamination and wear debris, insights into potential component issues can be gained early. Regular oil sampling and analysis are critical for the early detection of deterioration. It is recommended that analytical ferrography or filtergrams be specified as part of the analysis package of slow speed machines as these methods provide more accurate wear particle and contamination information for monitoring slow speed machines. The latest developments in online oil condition sensors show significant promise for slow speed machine monitoring.
3. Thermography: Thermal imaging is adept at identifying overheating problems and abnormal temperature distributions within machinery. In low-speed machines, where temperature changes might manifest more gradually, thermal imaging helps pinpoint localised heat irregularities or cooling inefficiencies. It should be noted that thermography is typically a late stage indicator of faults and cannot be relied upon for early detection of developing problems.
4. Ultrasound Condition Monitoring: Ultrasound monitoring has emerged as a preferred technique for slow-speed machines. Ultrasound waves can detect subtle changes in machinery, such as friction, impacting, cavitation, and leakage. Ultrasound provides a powerful means for optimising lubrication doses in grease lubricated machines. Ultrasound monitoring techniques enhance early fault detection even in low-speed conditions and supports predictive maintenance efforts.
By integrating a spectrum of condition monitoring techniques, maintenance teams can achieve a comprehensive understanding of machine health. These preferred methods enable the efficient and accurate monitoring of slow-speed machinery, facilitating timely interventions and assisting in ensuring the equipment reaches its intended lifespan.
