Current reliability calculations are predisposed to a single failure mode or mechanism and assume a constant failure rate, while research being carried out by the Center for Risk and Reliability at the University of Maryland implies that reliability is a function of the level of damage a system can sustain, with the operational environment, operating conditions and operational envelope determining the rate of damage growth.
One common way of controlling the state-of-health of a piece of equipment is to monitor its vibration signature. High quality data is required, so using high-resolution analog-to-digital converters (ADCs) to acquire and digitize the analog vibration signal is a key enabler to the arena of predictive maintenance.
Improper bearing lubrication or re-lubrication accounts for up to 40 to 50 percent of machine failures. By eliminating lubrication from machinery, OEMs can minimize the costs and risks associated with maintenance for the end user. At the same time, costs related to the proper disposal of oil can be eliminated and the initial expenditure for ancillary components and processes (grease lines, zerks, manifolds, etc.) can be decreased.
Drones and reality modeling can and should be part of every maintenance organization and every proactive maintenance workflow to dramatically enhance productivity and safety in infrastructure asset inspections.
Research into bearing failures1 shows that just over half of them are a result of contamination of the bearing oil. Clearly, it is essential to ensure that this is minimized and, if possible, eliminated to achieve the optimum bearing life necessary to improve equipment reliability.
Maintenance personnel go to work and do their jobs taking measurements and writing reports, often with great care and skill. Their reports and recommendations then travel into a deep abyss from which they never return. Sound familiar? This situation is all too common in the condition monitoring (CM) world.
In Evidence Based Asset Management (EBAM), theory and practice are joined to produce accurate outputs from statistical data and/or tacit knowledge through a process that includes state-of-the-art mathematical and statistical techniques that analyze, clean and process data. With data and knowing how to use it, maintenance managers can improve their standard maintenance practices. Applying principles of Evidence Based Asset Management to asset management decisions generates huge savings for companies as well.
Picture this: It’s Monday morning and you’re the maintenance manager of an industrial plant. On your desk is a printout of 432 open work orders and the operations manager is screaming because air compressor #2 just tripped for the third time this month. To make matters worse, you just remembered two of your technicians went fishing for the week. The question is: “What can this maintenance manager do to improve this situation?” The obvious answer is to go fishing with the two technicians. Maybe a better question to ask is: “What can be done to improve plant performance?”
It may seem that visual inspection goes beyond what thermographers are hired to do, but conscientious thermographers include notes and even images in their reports when they see code violations, broken equipment, incorrectly sized fuses, bad wiring, etc. A good thermographer will not ignore copper tubing used as a fuse, even if it looks fine in the infrared. Whether the inspection is insurance driven or a proactive stance, an infrared inspection and a visual inspection complement each other.
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Torbjorn Idhammar is the blog manager for MaintenanceWorld.com.
A maintenance and reliability management consultant, Torbjorn has spent 25+ years in the profession and provides advice to clients around the world.
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She is responsible for reviewing submitted content for the site and ensuring that it provides value to the readers of the site.
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