Author: Jerry Larkin, BSc (Hons), MSc, MIEI, AMIMechE, plant reliability engineer, GE Healthcare, Cork
The upcoming Meeta National Maintenance Conference on 'Risk and confidence in asset management' will take place in Engineers Ireland, 22 Clyde Road, Dublin 4 on 21 and 22 November 2013. Jerry Larkin will address six fundamental issues concerning failure that will enable delegates to eliminate, reduce or mitigate failures in equipment, processes or systems. This article presents one of these – the influence of time in the selection of equipment maintenance strategies.
One of the primary functions of any maintenance department is to maximise the plant and equipment performance, and thus contribute to the manufacturing/business effort – the equipment now becomes an asset. Preventing or reducing the occurrence of failure demands an understanding of the nature, sources and characteristics of failure.
Fortunately, many of these characteristics are universal and enable us to deal with failures in a more methodical way. For instance, the distribution or occurrence of failures over time is a key consideration for choosing the optimum maintenance strategy.
[caption id="attachment_8848" align="alignright" width="384"] Figure 1: Common maintenance strategies[/caption]
Fig.1 (right) presents the equipment maintenance strategies in a hierarchy to reflect their relative value and effectiveness. In many organisations, it may be only an aspiration to ‘design out’ – or to significantly modify the plant – in order to increase reliability and reduce failures. We will thus consider the two most common maintenance strategies: preventive (time-based) maintenance, and predictive (condition-based) maintenance.
PREVENTIVE VS PREDICTIVE - CHOOSING A STRATEGY
It is important to make a distinction between time-dependent and random failures. Time-dependent failure includes wear, corrosion or fatigue. A population of several identical plant items with a time-dependent failure mode (e.g. powder mills subject to abrasive wear) might exhibit the failure curve shown in red below in Fig.2 (below).
There is a tight distribution around a mean life of 18 months with no failure occurring before 12 months. It is easy to see that setting up a preventive maintenance routine to service these on a 12-month frequency should prevent failures, i.e. give another period of failure-free running. Time-dependent failure modes such as wear or corrosion exhibit a narrow distribution and lend themselves to time-based preventive maintenance.
[caption id="attachment_8854" align="alignright" width="663"] Figure 2: Time-dependent and random failure modes (click to enlarge)[/caption]
Random failures include load variation, operation, environment, process variation and component strength. These failure distributions are exemplified by the green and blue curves in Fig.2 (right) and may have no initial failure-free period.
For the group of items in blue, failures happen completely randomly with the same probability from one period to the next. One useful analogy is the human case where breaking a leg would be random failure, and hip replacement would be time-dependent.
For complex/critical plant having random failure modes, the most effective policy is predictive maintenance.
Preventive maintenance is actions carried out at predetermined intervals of time (or other criteria such as miles, cycles, etc), and is intended to reduce the probability of failure of a plant item. The maintenance action might be an adjustment, repair or replacement of a component, or it could be an overhaul of the complete assembly.
These preventive maintenance actions are called PM routines and are scheduled to occur at regular time (or cycle) intervals. The interval for a given plant item is derived from manufacturers’ recommendations, plant history, average life statistics or a combination of these sources.
For example, a plant item might have the following associated routines: