Predictive maintenance system cost

Most of the vibration-based predictive maintenance systems include the capability of recording visual observations as part of the routine data acquisition process. Since the incremental costs of these visual observations are small, this technique should be incorporated in all predictive maintenance programs. 

Expertise required to operate One of the objectives for using microprocessor-based predictive maintenance systems is to reduce the expertise required to acquire error-free, useful vibration and process data from a large population of machinery and systems within a plant. The system should not require user input to establish maximum amplitude, measurement bandwidths, filter settings, or allow free-form data input. All of these functions force the user to be a trained analyst and will increase both the cost and time required to routinely acquire data from plant equipment. Many of the microprocessors on the market provide easy, menu-driven measurement routes that lead the user through the process of acquiring accurate data. The ideal system should require a single key input to automatically acquire, analyze, alarm and store all pertinent data from plant equipment. This type of system would enable an unskilled user to quickly and accurately acquire all of the data required for predictive maintenance. 

Condition monitoring, life cycle costing and predictive maintenance procedures should all be considered during the preparation of the planned maintenance system. 

Recent advancements in microprocessor technology coupled with the expertise of companies that specialize in machinery diagnostics and analysis technology, have evolved the means to provide vibration-based predictive maintenance that can be cost-effectively used in most manufacturing and process applications. These microprocessor-based systems simplify data acquisition, automate data management, and minimize the need for... 

The capital cost of spectrographic analysis instrumentation is normally too high to justify in-plant testing. Typical cost for a microprocessor-based spectrographic system is between 30,000 and 60,000. Because of this, most predictive maintenance programs rely on third party analysis of oil samples. 

Effective preventive/predictive maintenance programs will be used to anticipate and predict maintenance problems in order to eliminate the uncertainty of expected breakdowns and high repair costs. Predictive maintenance wiU not be limited solely to the detection of failure but will proactively identify and eliminate the root causes of chronic problems. Preventive/ predictive maintenance programs will be adequately staffed to cover adl major assets within the operation. Maintenance wiU maintain current techniceil knowledge and experience for applying a combination of predictive technologies that is best suited for the specific application or system. 

Physical asset Use of rehabihty improvement technologies reliability-centered meiintenance, preventive/predictive maintenance, andknowledge-based/expert systems for meiintenemce of the physical asset. Asset faciUtation to gain maximum capacity at the lowest possible life-cycle cost. 

Productivity improvements can often be achieved with minimal investment. For example, a pump in production-critical service may fail once every 3 months. If each pump failure leads to production losses of 15,000, then the annual cost of this problem is 60,000. Investigation into the pump failures shows that the breakdown rate could be greatly reduced where a preventive maintenance system was to be implemented, so that problems can be addressed before the pump actually fails. It is predicted that the new failure rate will be once per year, equivalent to a loss of 15,000 per year. Hence the annual savings that flow from the preventive maintenance program are 45,000. If the preventive maintenance system for that pump costs say 10,000 to implement and 5,000 per year to run, then the net savings over a period of 5 years is 190,000 (ignoring the discounted value of money), and the return on investment is very high indeed. 

Building Automation and Control Systems Operation and Maintenance PRODUCT PREVENTIVE/PREDICTIVE MAINTENANCE PROCEDURES TROUBLESHOOTING AND REPAIR TIPS CRITERIA FOR REPAIR VERSUS REPLACEMENT HOW MUCH SHOULD MAINTENANCE ON MY CONTROLS COST SUMMARY... 

Maillart L.M. Pollock S.M. 2002. Cost-optimal condition-monitoring for predictive maintenance of 2-phase systems, IEEE Transactions on Reliability 51(3) 322-330. 

The RCM analysis, when used to identify maintenance actions, can reduce the probability of failure with the least amount of cost. This includes using monitoring equipment for predicting failure. RCM relies on up-to-date operating performance data compiled from a computerized maintenance system source. The data collected proves valuable when used in a failure mode, effects, and criticality analysis (FMECA) process to rank and identify the failure modes of concern (Table 4.12). 

In educational laboratories, the consumption of supplies per student should be quite predictable for any given course. The cost of equipment maintenance and replacement, however, is often underestimated. Unfortunately, in a college budget system, the reward for saving money one year may be a reduced budget the following year. 

Goals and Objectives of FEL The FEL work process must enable nearly constant consideration of changes as the work progresses. FEL phases must consider the long-term implications of every aspect of the design. Predictability of equipment and process system life cycle costs must always be balanced with operations and maintenance preferences, as well as the need for the project to maintain its profitability or ROI (return on investment). Additional important goals and objectives of FEL projects are as follows ... 

Table 1.4 lists the priority R D needs that were identified for burners, boilers, and furnaces. Essentially all of these needs require some amount of testing. These needs were generated from the following end-use requirements increased system efficiency reduced NOx, CO, CO2, and particulate emissions increased fuel flexibility more robust and flexible process control and operations better safety, reliability, and maintenance lower capital and operational costs faster, low-cost technology development and enhanced system integration. Coupled with these needs are some barriers to improvement financial risk, inability to accurately predict the performance of new systems, lack of industry standards, and the wide gap that often exists between the research done at a small scale that needs to be applied to industrial-scale systems. Testing is often required to address some of these barriers. 

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