Condition Monitoring/Predictive Maintenance

By using the latest condition monitoring systems and services, manufacturing companies can reduce unforeseen breakdowns. Schaeffler UK's managing director, Kate Hartigan, explains

When owning and managing high value items like cars or homes, most people are comfortable paying insurance premiums. So, surely manufacturing companies need to ensure their high value capital goods are adequately insured against unforeseen breakdowns? After all, lost production could equate to tens of thousands, even hundreds of thousands, of pounds per day. Although the cost of machine components like bearings and motors is small compared with the total machine price, the cost of production downtime and consequential losses resulting from a component failure, are often significant.

Take a steel or aluminium manufacturing plant. The typical cost of production downtime is

For many organisations today, the most important issue is how to reduce costs to compete profitably in global markets.  The two key approaches to manufacturing cost reduction are Lean and TPM (Total Productive Maintenance).Lean reduces costs by eliminating waste in the end to end process, following Henry Ford's original dictum "the longer anything is in my factory, the more it costs me".

Total Productive Maintenance reduces costs by eliminating losses in equipment based processes - once you have a lean flow, the key is to run equipment as efficiently as possible. Both Lean and TPM are based on a foundation of 5S and Continuous improvement (Kaizen) and our success is based on helping companies develop this foundation as well as applying more advanced Lean and TPM approaches.

Plants can improve their efficiency and reduce costs by performing calibration history trend analysis. By doing it, a plant is able to define which instruments can be calibrated less frequently and which should be calibrated more frequently. Calibration history trend analysis is only possible with calibration software that provides this functionality.

Manufacturing plants need to be absolutely confident that their instrumentation products

If one could get the right mix of preventive maintenance (PM) and corrective maintenance (CM), things might not be so bad. If CM could be reduced to zero, that would be grand, but that is not going to happen. Amid the propositions of RCM, TPM, RCA, the Pareto rule, best laid plans, etc., one still must contend with randomness. When our programs result in high levels of performance, the limiting factor is often randomness. Using the concept of randomness in analyzing equipment failure events is helpful in establishing the realistic limiting factors in PM and CM program development and management.

Before getting enthused about understanding the technical milieu of equipment failure modes and causes, failure intervals and maintenance task development in recovering a dysfunctional maintenance program, first look for an opportunity of a large reward for a small resource expenditure.

One large reward, for example, could be a 50% reduction in corrective maintenance resulting from a program developed and implemented in-house (small resource expenditure). That 50% number is not unrealistic.

Predictive maintenance (PdM) techniques are commonly used on motors and drives. But how often is the power to the equipment inspected? Adding basic power quality measurements to equipment maintenance procedures can head off unexpected failures in both the equipment and the power system.

Cost savings
Insurance claims data in the NFPA 70B maintenance standard show that roughly half of the cost associated with electrical failures could be prevented by regular maintenance. A study published in IEEE 493-1997 says that a poorly maintained system can attribute 49 percent of its failures to lack of maintenance.

To determine the cost of a failure, it helps to consider three key categories:

The first step toward realizing this goal is to figure out where you are presently. Determining the present baselines and gaps that exist in your processes and organization is best accomplished by performing a reliability assessment. This exercise will point out the areas within your business that need to be addressed from a reliability perspective. All facets of your organization must be analyzed. We have identified 21 key elements that, when integrated, provide the basis for reliability excellence (Fig. 1). Assessing the current state of each of these elements will identify the gaps that exist and provide the basis for a master plan of improvement.

Master plan

A master plan is the tool that tracks what must be done, who will do it, and in what time frame the task(s) will be completed.

The master plan should initially outline the process-launching activities. These activities include reviewing the master plan and initiating the improvement process, developing the return on investment, establishing a reliability excellence leadership team, and identifing focus teams and support resources.

The definition and use of Overall Equipment Effectiveness over the years has been widely debated.

Many practitioners have found that OEE has several uses and definitions which have led to
considerable confusion when comparing machine-to-machine, plant-to-plant, or company-tocompany.
Unfortunately, OEE was not designed to make comparisons from machine-to-machine,
plant-to-plant, or company-to-company, but it has evolved to these common levels of misuse. OEE is not a statistically valid metric, but it has been used as such for years.

The purpose of this article is to discuss the original intent of Overall Equipment Effectiveness as a metric and a measure. I initially learned OEE from Seiichi Nakajima, the

Leading maintenance repair and operation (M.R.O.) organizations are leveraging a range of best practices for optimal asset performance. Such methods encompass maintenance best practices that drive efficiencies and equipment life cycles, as well as cost-cutting ideas from allied industries like field services.

Efficiently run field service teams have, for years, used and benefited from automated scheduling applications that deliver the right service tech to the right location at the lowest cost. They have cut wasteful travel and duplicate visit expenses to complete more service calls that better serve their customers and extend asset life.

Conversely, asset managers are well aware that more work order information and background, with more details, in the hands of technicians at the point-of-performance is a key productivity driver that means improved equipment performance. Data captures reported electronically also are driving better decision-making by management, in addition to fueling accurate and timely compliance reporting.

Risk Based Inspection (RBI) is a method for using risk as a basis for managing an inspection programme. The concept of risk is used to target inspection and maintenance resources at areas of the plant where they can have the greatest effect in reducing risk, the occurrence and consequences of unplanned failures and to reduce the cost of unproductive inspections. Risk is a function of both the likelihood of a failure and the consequences of the
failure.

An RBI programme must include updating from results of inspections,monitoring and process changes and will specify an inspection requirement and frequency. The inspection requirement may be satisfied by a number of alternative inspection methods.

Tischuk Enterprises (UK) Ltd has developed an integrated software systembased on the Tischuk Operational Criticality Assessment (OCA) system of analysis. This is designed to assess risk in a simple and cost effective way and is a fundamental part of the development,implementation and management of risk based strategies. OCA uses a 3 x 3 risk matrix to represent the different levels of risk.