Overview of predictive maintenance of equipment:
From the probability curve of equipment failure, we can find that if the regular maintenance method is adopted, during the running-in period and the aging period, it is easy to cause frequent accidents due to insufficient maintenance; while in the stable operation period of the equipment, it will be over-maintenance. Inefficiency or equipment over-maintenance.
(PDM) is a state-of-the-art preventive maintenance program. Predictive maintenance of equipment uses infrared thermal imaging cameras, vibration analyzers and other testing equipment, by detecting the operating parameters such as temperature and vibration of the equipment, and comparing the measured parameters with the standard operating state parameters of the equipment to determine whether maintenance is required. And how to arrange maintenance work in a targeted manner.
Maintenance costs, as determined by general plant accounting procedures, typically constitute the bulk of total operating costs in most plants. In the United States, traditional maintenance costs (ie, manpower and materials) have risen sharply in the past 10 years. In 1981, US factories spent more than $600 billion on maintaining their critical installation systems. In 1991, this cost had risen to more than $800 billion, and in 2000 it was a record-breaking $1.2 trillion.
These data show that one-third to one-half of these costs are wasted due to ineffective maintenance management methods. The American industry can no longer tolerate this incredible inefficiency, and they want to participate in competition on the world market. There are still relatively few data on this in other countries, but we believe that the situation is basically the same. The main reason for this inefficient maintenance expenditure is the lack of actual data on when and what maintenance is needed to maintain, repair or replace key machines, equipment and systems within the plant or facility. Typically, maintenance organizations do not track equipment performance, performed maintenance tasks, failure history, or other data that can (and should) be used to prevent premature failures, extend the life of critical plant assets, and reduce their The life cycle cost task is planned and arranged. Conversely, in many cases, maintenance schedules are still determined by equipment failures and the instincts of maintenance personnel, who can arbitrarily determine the type and frequency of routine maintenance.
For example, most facilities that use thermal imaging inspections are inspected every six months or six months. This is a purely arbitrary decision without any actual data. In addition, the middle and company management ignored the impact of maintenance work on product quality, total operating costs, and more important bottom line profits. The general opinion is that “maintenance is a damn thing that has to be done” or “there is no way to improve the maintenance cost situation.” In 10 or 20 years, this statement may be correct. However, the development of microprocessor or computer based instrumentation and maintenance management systems provides a means to optimize maintenance efficiency.
Microprocessor-based instruments such as infrared monitoring and vibration monitoring can be used to monitor the operation of critical plant equipment, machines, and systems. The information obtained from these instruments provides a means to effectively manage maintenance operations. At the very least, they can reduce or eliminate unnecessary repairs, prevent catastrophic machine failures, and reduce the adverse effects of ineffective maintenance operations on manufacturing and production plant profits. When their capabilities are fully utilized, these instruments provide a means to optimize overall plant performance, machine useful life, and life cycle cost of facilities and their assets. Computer-based maintenance management systems provide historical data and methods for using data derived from predictive maintenance techniques such as infrared monitoring and vibration monitoring. Industrial and processing plants typically use two types of maintenance management, namely “run to failure” and “preventive maintenance.”
Run to failure management
The idea of running to failure management is straightforward. Repair the device in the event of a malfunction. This “maintenance without failure” machine maintenance method is a major part of the maintenance operation since the establishment of the first manufacturing plant, which sounds reasonable. A plant that operates to failure management will not spend any money on maintenance until the machine or system fails. Running to failure is a reactive management technique that waits for a machine or equipment failure before taking any maintenance action. Rather, this is a “no maintenance” management approach. It is also the most expensive maintenance management method. However, it should be said that very few factories adopt a true operation to failure management method. In almost all cases, the plant performs basic preventive maintenance tasks—lubrication, machine adjustments, and other adjustments, even in a managed-to-failure management environment. However, in this management mode, the machine and other plant equipment will not be restructured or repaired before the equipment fails.
Research conducted by the US Federal Energy Management Program (FEMP) estimates that a properly functioning predictive maintenance program can provide 30% to 40% savings compared to adaptive maintenance. Other independent surveys show that, on average, an industrial predictive maintenance program can result in the following savings:
Return on investment: 10 times
● Reduced maintenance costs: 25% to 30%
● Fault elimination: 70% to 75 %
● Shorter production time: 35% to 45 %
● Increase in production: 20% to 25 %
To calculate savings in your facility, you first need to estimate the cost of unplanned equipment failures: human resources factors, departmental costs, and revenue loss for a particular production line. Then, after your thermal imaging maintenance plan is running, start tracking the savings. Maintain a record of equipment resource availability, production throughput, and maintenance cost allocation and total maintenance costs over time. These numbers help you calculate the return on investment for thermal imaging maintenance