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Issues and aims (visions) for JIPM: No.3
1. Studies on "progress on the current TPM"
1-1 Studies on equipment life prediction and replacement of equipment
There are two types of aging of equipment life.
- Aging of equipment itself: the product life of the equipment itself comes to an end (i.e., aging of parts, fatigue in spindles and foundation, paint deterioration of buildings, towers and vessels, and increase in discontinued products, etc.)
- Aging of functions independent from aging of the equipment: the product life of the equipment itself has not ended (incapacity to meet required quality, higher requirement on quality, obsolescence with products in the market, etc.).
Associated with these types of equipment lives, companies replace or newly build equipment. Therefore, it is not too much to say that the accuracy in equipment life prediction can change a corporate culture, and can even make or break its business activities.
In terms of theory, the equipment life is organized and easy to follow, but in reality, it is very difficult to judge. This study focuses on this equipment life prediction to explore how to increase the accuracy in equipment life prediction. The focal point in this paper is "aging of the equipment itself" as described in 1. above.
Details of the studies
Repair costs greatly depend on aging of equipment, because the cost of repair and updating accrues according to the equipment life. The determinants of the updating are equipment life prediction, and time and scope for updating. The study on prediction will also cover the methodology for determining the probability of troubles or the size of the risks.
- Studies on technology for equipment life prediction
The state-of-the-art technologies for equipment life prediction have greatly advanced. Above all, those related to vibration are organized and employed in many rotating machines. Also, things like oil control and data on current and power are used to estimate the equipment life.
However, if the equipment has been in use for 5, 10 or 30 years, it is quite difficult to predict the degree of deterioration of the equipment month by month. Due to this, some companies have given up equipment diagnostic technology in spite of their initial interest.
Also, a lot of record data on equipment have been consolidated beyond corporate boundaries. However, estimation with this data is only valid for the average equipment lives, but not the equipment life of their own company with 100% validity, because it is difficult to make adjustments to reflect the company's background history of corrections, such as repair records, presence or absence of failures and operating conditions. The problem with this is that it is not possible to accumulate data good enough to apply directly to their own equipment.
From these facts, we aim to conduct studies focused on methodology to judge equipment life and on the equipment diagnostic technology and the data analysis system, so that they will be easy-to-use, convenient and highly reliable.
- Consideration of risks and studies on equipment life prediction
Even with predicted equipment life, a trouble can occur unless it is treated properly. This means that another viewpoint to determine a timing for replacement is how to think about the size of risk and countermeasures in case of trouble. In other words, equipment that causes little impact when in failure can be repaired after the failure, but those that may trigger serious disasters should be repaired before they break down.
In so doing, what kind of risks should be considered is an important issue. In this study, issues such as how to think about these risks, which types of risks needs to be predicted, and how to relate the prediction with actual management practices will be the themes for discussion.
System and period of the research
Regarding 1. above, the study group on state-of-the-art maintenance technology has been set up. In this study group, the scope of research will not stay within the state-of-theart equipment diagnostic technology, but will expand to the surrounding systems to support the diagnosis, such as EAM, PAM, RCM and RBM, to perform activities to pursue proactive maintenance. It is an important research task to understand the current situation correctly and consider how to reflect that information on maintenance. In our perspective, this will be applied not only to the process industry, but also to a wide range of industries such as assembly processing, automobiles, semiconductors, and food.
Regarding 2., we would like to take it as an issue for fiscal 2006 onward. In this fiscal year, the relationship between risk & loss, maintenance and management within the framework of MOSMS will be under study, which will then be put to further discussion in fiscal 2006 onward.