What is Predictive Maintenance?
Vibration Analysts Inc provide Predictive Maintenance Services which utilize data, like machinery vibration data, to anticipate machinery failures before they occur. This knowledge enables proactive maintenance to be conducted at optimal intervals, minimizing downtime and the repair costs associated with unplanned failures. By performing corrective maintenance before machine faults degrade or progress to failure, user can realize a significant cost savings compared to the traditional time-based maintenance approach of ‘fixing and repairing everything’.
More to the point, EVERY machine will inevitably experience a fault that can lead to a failure if not corrected. It’s not a matter of “if” but “when”. When performed correctly, regular maintenance will extend a machine’s life, however continued wear and tear on moving parts will necessitate an eventual replacement. In the machinery-dependent factory, halting the assembly line to perform bearing replacements and oil changes consumes valuable time, finances, and resources, diverting them from activities and possibly reducing production.
Maintenance Optimizations – The OLD Way:
Up until the latter half of the 20th century, interruptions caused by machinery failures and the need to make emergent repairs were unavoidable for most operations. That’s because the failure to maintain scheduled maintenance activities increased the risk of catastrophic equipment failures, potentially resulting in even greater losses of time and money to make repairs. To prevent machine failures, the “replacement intervals” were conservatively estimated based on parameters such as the number of fan revolutions or the expected lifespan of a bearing based on previous hours-to-failure. The primary drawback of adhering to scheduled maintenance activities was in its reliance on the “estimated” lifespan of various components. Unfortunately, many environmental and operating conditions (pressure, rpm, quality and quantity of lubricant, humidity, temperature, airborne particulates, load, and others) had the potential to significantly change the rate of wear and degradation. Consequently, maintenance schedules were based on overly conservative estimates to minimize parts failing during operation, often resulting in many parts and machines being replaced unnecessarily.
Why Predictive Maintenance is Superior – The NEW Way!
Predictive maintenance empowers Vibration Analysts to monitor the real-time condition of a machine and recommend corrective actions when the machine reaches the end of it’s RELIABLE operating life. By performing periodic monitoring and trending changes due to operational nuances, a precise assessment of a machine’s health and condition can be made. In fact, when vibration data is trending on a monthly basis (recommended) it can be used to detect and identify the machine’s initial degradation at very low levels of concern. That means not only can machines continue to be operated for a reasonable time, the vibration data can be used to accurately identify what the degradation is and what needs to be corrected.
Vibration analysis is currently the most precise and comprehensive method for detecting and identifying machinery health issues. And by periodically monitoring a machine’s vibration data, a skilled Analyst can precisely assess it’s current health at any given moment. Equipped with this insight, machinery owners and maintenance staff can select the optimal and best cost-effective approach for implementing repairs. There is an initial investment to setup and maintain a vibration program, however the industry has proven the long-term benefits greatly outweigh these initial costs. Furthermore, this vibration monitoring process ultimately becomes a self-sustaining investment and VAI clients typically report a “Return on Investments” of $ 6.38 for every dollar spent on VAI’s services.
Business owners who extend the lifespan of their machinery save money. Factories that circumvent unexpected and expensive shutdowns save money. And companies that stay informed about the condition of their machines (and avoid failures) save money.
