How condition monitoring improves reliability

In some industries, such as automotive assembly, downtime can cost up to £17,000 per minute (a mind-blowing £1 million an hour). Some unplanned downtime may be beyond the control of a manufacturer – caused by infrastructure failures, human error or even natural catastrophes. However, there are ways to reduce the risk.
Automated machinery can dramatically increase effectiveness by speeding up production time and reducing the potential for human error, therefore increasing overall equipment effectiveness (OEE). According to the Annual Manufacturing Report 2017 by Hennik Research, two-thirds of UK manufacturers have made investments in automation in the past 12 months. However, once the investment has been confirmed, the installation of automation into production needs to run smoothly.

Traditionally, maintenance has been done on a fixed schedule, replacing items at constant intervals. Although the intervals are usually based on established knowledge, that is only part of the story – as we all know, parts wear out at different rates for all kinds of reasons. Constant intervals do not take into consideration the unique and varying circumstances of a machine being maintained.

A key problem is the waste of time and resources involved in routinely carrying out preventative servicing procedures which may not actually be necessary. Traditionally, service intervals are based on machine operating hours or something similar, but in practice, machine failures tend not to follow such predictable schedules. By the same token, changes in the machinery’s operating conditions may have resulted in components failing before the scheduled service was due. Either way, this practice generates unnecessary downtime, labour and replacement part costs. In fact, unnecessarily intrusive servicing activities can even induce premature component failure.

Predictive maintenance is about getting the most life out of equipment while minimising the risk of failure. It ensures that parts are used to the end of their natural life, but risks downtime due to delays in delivery of replacements or additional costs due to higher stock levels.

Ideal combination

The ideal combination is predictive maintenance with just-in-time supply chains. Condition-based – or predictive – programmes for the care of critical production and processing machinery deliver cost-efficiency, reliability and productivity advantages over traditional routine preventative maintenance activities.

Instruments for condition monitoring will look for variances in acoustics, vibrations, thermal output, motor current signatures and other areas, which help determine maintenance schedules and reactive steps needed to keep machinery running optimally.

The process involves gathering large quantities of data – such as maintenance records and data from sensors on the equipment. Collecting and archiving company-wide production data is hugely beneficial, but can be difficult to obtain. Additional data sources, such as sensors on the equipment, are important to build the big picture.

One of the biggest challenges is gathering and interpreting so-called unstructured data, such as free text in maintenance records, design specs, test data from failed equipment, or even comments on social media or Google searches.

Video use is also accelerating in data collection. Video cameras are already used in many test and measurement applications throughout industry.

“There is no longer any question that recording video data in parallel to tactile sensors or digital bus signals is becoming more and more attractive to users," says Christof Salcher, product manager Instrumentation at HBM. "Video supports traditional sensor data and is becoming a valuable source of additional information, making the room for interpretation even narrower in testing.”