Condition Monitoring (CM) and Computerized Maintenance Management Systems (CMMS) as part of Enterprise Resource Planning (ERP) systems and asset optimization have evolved and coexisted as separate disciplines with respect to information management. Here we examine the factors associated with the success of asset optimization, the impact of integrating CM and CMMS within the ERP system and the role of oil analysis in the process.

Optimizing Machinery Asset Utilization
Economic forces are dictating maximum output for minimum input. Companies that are achieving this goal successfully are those that are investing in the people and equipment required for effective asset management. In successful cases multi-disciplinary work forces manage profit centers incorporating various stages of the manufacturing and delivery process. Supported by reliability engineers and consultants, these profit center teams consist of business managers, operations personnel, maintenance personnel, and quality managers. The profit centers are accountable for the safe and efficient operation of their production area and the cradle to grave responsibility for the welfare of their equipment assets. To be successful these skilled team players must speak on common terms and work together to manage the utilization of machinery assets.
The team is charged to accomplish the following objectives:

  • Exceed specified minimum operating safety standards
  • Exceed minimum production levels
  • Exceed specified minimum quality grade levels
  • Stay within defined operations and maintenance budgets
  • Comply with defined safe working limits for noise and vibration
  • Comply with defined maximum levels of hazardous substances either by emission or disposal
  • Ensure adequate maintenance inventory at minimum cost
  • Ensure adequate financial insurance at minimum premiums
  • Maximize mean time between overhauls / failures
  • Exceed the specified minimum asset lifetime
  • Minimize asset disposition or salvage costs

To establish accountability, internal groups within the organization (engineering, maintenance, operations, purchasing, reliability, risk-management, etc.) must set initial performance specifications. Likewise, evaluation of the team’s performance will require efficient tracking of data to compare against the pre-set targets. These two statements presuppose that the information exists, is readily obtainable by those that might require it, and that information transfer is bi-directional, providing a feedback loop.

Machinery Information Required for Optimization
Figure 1 illustrates the typical information access paths within an organization, as well as the role each nodal point plays in achieving “optimized asset utilization” through accountability and performance evaluation. The following data “spokes” contribute to effective asset utilization:

Engineering Design and Configuration Management - Information exchange is vital to ensure that the components procured optimize cost against expected load. Too much stress / strength / interference drives up an asset’s life-cycle cost. n Safety, Regulatory and Insurance Compliance - Limits and standards must be set in accordance to regulatory requirements and company policy. Performance must be evaluated accordingly.

Operations Planning - Evaluation of past and present performance is key to determining potential performance. Production planning will also need to understand changing daily factors in other profit centers that may have impact as well as maintenance requirements within the profit center. Quality of both in-production and finished goods will also be a determining factor.

Operations Execution and Feedback - Enterprise Resource Planning (ERP) systems tie together all aspects of the supply chain, usually within a single data warehouse structure. This allows an operations execution plan to take into consideration any current backlogs within the current production stream. So-called Distributed Control Systems (DCS) allow feedback of process parameters into the execution plan.

Reliability Planning - Strategies such as Reliability-Centered Maintenance (RCM) will optimize management’s selection of an equipment maintenance plan that combines the following:
- Condition-based maintenance (Proactive or Predictive maintenance),
- Time or cycle-based maintenance (Preventive maintenance),
- Failure-based maintenance (Reactive maintenance).

Reliability-Driven “Maintenance” Execution and Reliability Feedback - This is where the maintenance professional has the greatest impact on profitably. It is through the Condition Monitoring (CM) practitioner’s clear recommendations and forecasts (rather than raw CM data) that the optimization team can make informed decisions.

Machinery Asset Health Analysis - Effective reliability planning will be dependent upon meaningful machinery
information. The data may include process data as well as data captured from transient systems and will be reliant upon a number of analysis technologies including lubricant, vibration and thermography.

Inventory, MRO Purchasing, and Financial - Maintenance, Repairs and Operations (MRO) purchasing will determine an optimized level of inventory to minimize cost of storage and maximize parts availability.

Open Path to Data Integration
Integrating CM and CMMS Systems for Comprehensive Asset Information
CM is best defined as a maintenance strategy. CMMS is best defined as a system to effectively manage the execution of maintenance. Understanding the respective roles of CM and CMMS will provide a clear vision of the benefit of integrating the two systems for maximum effectiveness.

As defined within the context of reliability planning, CM is a recognized maintenance strategy that achieves significant savings. CMMS, defined within the context of reliability execution (maintenance) and feedback, can be considered an organization’s information warehouse for maintenance knowledge and procedures.
Integrating CM and CMMS delivers the following new benefits or enhancements:

  • More effective and automated implementation of maintenance strategy
  • Improved accuracy of CM analysis through information feedback
  • Identification of repetitive failures for root cause analysis
  • Effective communication of machinery health information throughout the enterprise

MIMOSA and Its Open System Information Exchange Model
The Machinery Information Management Open Systems Alliance, MIMOSA, is a not-for-profit organization composed of progressive users of industrial production, process, and manufacturing equipment as well as the suppliers and service providers of operation, control, and maintenance information systems. MIMOSA activities are directed toward gaining maximum efficiency, value, and benefit from equipment assets across a broad range of industries. This is being accomplished by facilitating the concept, development, dissemination, and justification of efficient, open, electronic exchange conventions for design, purchase, performance, operation, maintenance and condition information.

A MIMOSA open system is one in which equipment asset software components are able to communicate and exchange data automatically without any proprietary or supplier-specific interface protocols. Figure 2 shows the current MIMOSA open systems information exchange model for product engineering data, ERP, control, condition monitoring, maintenance, and production systems.

The Role of Oil Analysis in the Optimization
A key benefit derived from integrating CM and CMMS is the identification of repetitive failures for root cause analysis. This, in essence, is proactive maintenance, a philosophy that is recognized as having a significant and beneficial impact on the profitability of a company. Lubrication management plays a major role within the context of proactive maintenance, and this incorporates the oil analysis strategy. Much has been written about the benefits to be gained from a proactive oil analysis focusing on the root causes of lubricant degradation and system wear that leads to failure. The benefits are documented and clearly defined by a number of leading organizations. The aim of this paper is not to revisit this, but to describe specific areas where oil analysis fits within the ERP system through the integration of CM and CMMS to fully achieve the benefits of proactive maintenance.

On-Site or Off-Site?
Increasingly, organizations are taking responsibility for the oil analysis aspect of their CM program to ensure its effective integration within the CM strategy. Historically oil analysis was an off-site strategy handled by commercial laboratories. However, due to management’s new view of oil analysis as a core asset management tool, on-site oil analysis is experiencing a trend of rapid growth within many industries for routine trending of oil health and cleanliness. This is not to say that the value of commercial laboratories should be ignored. These laboratories play an increasingly significant role dealing with the predictive aspects of oil analysis; using sophisticated and expensive instruments to better understand the nature of the impending failure and its progression. Increasingly however, laboratories are being viewed as an expert source for utilization on an exception-only basis and for periodic analysis of the lubricant’s properties. All lab generated oil analysis data should be stored and correlated within the same CM database as the on-site activity and other CM techniques.

Which Measurement Parameters?
Because of the root cause focus of proactive maintenance, the areas of interest in an on-site CM strategy should be at the very least:

Solid Particle Counts - associated with the root cause of system wear and additive depletion.

Viscosity - associated with determining either fluid dilution, incorrect lubricant or as an indicator of the lubricant’s remaining useful life.

Moisture - preferably maintained below the saturation point to avoid additive scrubbing and system corrosion.

Total Acid Number (TAN) or in the case of diesel engines,

Total Base Number (TBN) - associated with the remaining useful life of the lubricant.

Ferrous Density Indication - associated with the amount of wear metals in the lubricant.

Temperature - associated with lubricant damage, or incorrect system specification such as alignment problems.

Parameters that are often logged but not correlated in a CMMS system may include process or consumption variables:

Lubricant Make-Up or Consumption - unacceptable levels are usually associated with a poor lubricant strategy in terms of handling and contamination control.

Filter Life and Rate of Blockage - when specified correctly, filters should, in most applications, have at least a six-month life, and sudden change in the rate of filter blockage may indicate a system fault.

System Operating Variables Such as Temperature, Pressure, Power Demand, Emissions, etc. - changes in these values may indicate deterioration in performance that could adversely impact productivity and quality.

The Case for Low Cost Sensors?
While on-site measurement tools currently exist that are capable of permanent on-line analysis of select oil analysis parameters, few, if any, are available at a price that allows permanent mount for surveillance except on very critical or capital intensive systems. However, with the current progression in technology, more low-cost sensors that resemble vibration sensors are appearing in the market, such as relative humidity sensors. These will become more commonplace and initially will form part of a walk-around route using a hand-held data collector that can plug into and provide power to the sensor. However, the eventual goal will be to provide these as part of a networked surveillance system.

The purpose of these sensors will be to measure the system conditions, as indicated above, in real time and provide the analyst with much more information across the life of the machine with significant benefits to the reliability and engineering groups. In addition, the low-cost will mean that the frequency of laboratory analysis can be reduced. Apart from low cost, the use of on-line sensors will reduce the amount of time and manpower that is required to manage the oil analysis program, while minimizing the health and safety hazards associated with the sampling, handling and disposal of lubricants. Figure 3 illustrates how on-line sensors that are employed for automated surveillance or walk-around data collection combine synergistically with on-site and laboratory oil analysis techniques.

Analyzing the Data?
Currently, oil analysis software systems rely heavily on human interpretation for reaching meaningful conclusions that lead to effective decisions. In the future, artificially intelligent software systems will perform the initial analysis and screening, leaving the human analyst to address the exceptional situations. For example, the system that may monitor a single or primary solid particle sensor, but on alarm condition, will then access data from other secondary solid particle sensors mounted on the system. This may determine whether the filter has failed or a work-end component is in the failure initiation mode. Alternatively, information from the moisture sensor may indicate that the cause of increased solid particles is related to moisture.
Having said that the permanent mount sensors will reduce the effort involved in current on-site sampling, this should not be perceived as a reason to eliminate the most effective component in the oil analysis strategy, the oil analyst. Artificial intelligence algorithms provide only limited capability in assessing problems. Hence the analyst will continue to play a significant role in the asset optimization process through his understanding of the correlation between data sets and other CM techniques. However, the artificial intelligence software will free the analyst to focus on critical issues across the plant and to strategically evolve the program through the development of better lubrication strategies.

Integrating CM with CMMS and ERP?
The value of integrating oil analysis, not just with other CM technologies but with the CMMS parameters, becomes immediately apparent when one considers how inter-dependent the two are. Reducing the root cause effects through CM will have immediate benefits to the life of lubricants and filters, as well as maintaining design specification operating parameters to achieve maximum productivity and quality. At the same time, analyzing the CMMS measurements will highlight areas of concern, such as excessive lubricant consumption or short filter life, and help focus the CM activity accordingly.

Integrating all of this into the asset optimization process will identify where design specifications are exceeded so that the reliability planning and feedback loop is completed. In addition, engineering can learn from the exercise by understanding past performance and enhancing design specification to meet increased production demands. Furthermore, MRO inventory and purchasing can then maintain the correct balance of spares to ensure timely rectification with minimum financial penalties on stock holding.

Another benefit for the analyst, irrespective of the technique, is the ability to track work order progression. This will help the data analysis to better evaluate the success of the corrective work if the job’s completion has been recorded in the system. In addition, the actual maintenance work order is more efficiently requested by the analyst straight into the CMMS system rather than via communication with maintenance planning.

Education and Lubricant Management?
While the oil analyst has a policing role with respect to whether the specific targets are maintained, within the profit center each and every person has a responsibility to ensure that the lubricant is handled correctly. Whether this be the lubricant’s purchase, storage, dispensing or the subsequent disposal, it is important that each stage minimizes impact on the lubricant’s quality and cleanliness, and on the environment. Therefore, education will play a major role in ensuring that all individuals are aware of best practice and how their actions can contribute to or counteract these efforts.

The success that oil will have in asset health optimization will be reliant upon a number of issues. First, permanently mounted sensors, irrespective of the technology, will need to be low cost, although as demand from industry for sensors increases so the price will fall as witnessed in the information technology industry during the 90’s. Second, monitoring software will need smarter algorithms to better interpret data as it is gathered from the sensors. Third, software will need greater levels of compliance to allow exchange of data between different packages, and to this end, the Maintenance Information Management Open Systems Alliance (MIMOSA) Group will be a driving force. Finally, education will vastly influence success as company personnel, rather than a CM department, take a group responsibility in achieving the goals.

All of this will only be effective if the whole is tied together with information flowing bi-directionally between various resources within the company. This, the over-riding success factor, will be where team play has a major role.

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Wetzel, Rick (1999), Integrating Machinery Condition Information with CMMS to Support Plant Asset Optimization. (Entek IRD International)

Troyer, D (1995), Three Dimensions of Equipment Condition Monitoring with Oil Analysis. P/PM Technology, April.