Grading condition-based asset care
Published: 02 April, 2019
PWE looks at how a staged approach to condition-based maintenance, with asset criticality defining three stages of sophistication - ‘basic’, ‘better’ or ‘best’ – is able to meet operational needs and budgets.
The market for machine condition monitoring is growing considerably faster than the average growth in global GDP. However, the extent to which an enterprise is prepared to invest in such technologies will depend on a host of factors, asset criticality perhaps foremost among them.
According to a recently published report the global machine condition monitoring market, valued at US$1,872.2 million in 2017, is expected to reach a value of US$2,529.7 million by 2023, representing a compound annual growth rate of 5.14% over the five-year period 2018-2023. The report emphasises that machine condition monitoring has recently gained increased significance as companies continue to focus more on asset utilisation with the aim of increasing their levels of productivity. Improving equipment performance and productivity through predictive maintenance and enhancing equipment reliability by effectively determining when equipment is likely to fail in service are among the many factors that are boosting the growth of the machine condition monitoring market.
Both condition-based and predictive maintenance can take a variety of forms, and the level of sophistication deemed necessary for a particular facility will be dictated by a host of factors. Not least being the costs of implementation, the availability and skills levels of onsite maintenance staff, and the impact of machine failure on personnel safety, downstream processes, profitability and ultimately brand reputation.
Phil Burge, marketing and communications manager at SKF explained to PWE that it has developed a staged approach to condition-based maintenance, with asset criticality defining which level of sophistication provided by each of these stages – which, for the purposes of this article, let’s call ‘basic’, ‘better’ and ‘best’ – is most appropriate to a particular application. Whichever the final choice, the solution should meet both current and future requirements, as well as being affordable to manage and easily expanded upon should this be needed at a later date. We will take a look at each of these asset care stages in turn:
Basic: Where traditional methods of preventative maintenance can often lead to unnecessary machine inspections, condition-based maintenance identifies potential wear or faults as they develop. Thus allowing more convenient maintenance scheduling. Basic condition-based maintenance makes extensive use of handheld vibration and temperature monitoring tools, commonly used by maintenance technicians during scheduled or impromptu walk-around inspections.
Readings taken by these devices may be transmitted via Bluetooth to a smartphone or tablet running an entry-level app that stores and shares machine health data. Users can set their own alarm thresholds in the app or use the app’s stored machine condition profiles against which they can assess the condition of the machine under inspection. In addition to handheld devices, permanently installed sensors equipped with colour-coded LEDs can be used to provide an at-a-glance indication of machine health.
Manual collections of machine condition data are, by definition, periodic and will be subject to occasional lapses in concentration by inexperienced operators, leading to the possibility of missed events or erroneous recorded readings. Moreover, these data collection methods become less efficient as the number of inspection points increases, which places a burden on resources and manpower.
Thus, while ‘basic’ approaches to machine condition monitoring are of considerable worth to the smooth running of a manufacturing facility, there are limitations to their effectiveness. It may be prudent to go one stage further, particularly if certain vital production assets demand the closer attention of more highly qualified staff.
Better: Machine operators work in close proximity to their equipment, so they are usually the first to detect even the slightest changes in process conditions and machinery health. However, their observations often go unreported, or are not effectively acted upon, leading to machine failures, unplanned downtime and higher operating costs. In this case, it makes sense to make use of this valuable (albeit undervalued) source of machine health information and ensure that it is easily collected, analysed and acted upon.
This can be achieved by making use of more sophisticated apps which extend the diagnostic capabilities of a handheld instrument, allowing a machine operator to gather machine condition data and disseminate it more effectively to the local maintenance team and beyond to remote diagnostic sites. The apps that fall into the ‘better’ category are more sophisticated in terms of connectivity, enabling measurements to be uploaded to a remote Cloud server. This can either be accessed later by maintenance staff for analysis on a desktop, or even viewed by remotely located experts should a more professional assessment be required.
Taking it a stage further, ‘better’ asset care may also involve the installation of fixed, multi-channel machine health monitoring systems, located either in a central control cabinet or alongside the monitored machine. These systems are capable of storing large amounts of data, which are accessed via the plant’s fieldbus network for local or, as is becoming more prevalent in these days of lean maintenance departments, remote analysis. They bring affordable machine health monitoring to a much wider industrial user base and may even include useful features such as ‘event capture’ which will have particular appeal to machine tool users requiring a cost-effective crash detection capability.
Best: When handheld or periodic data collection instruments are deemed insufficient, possibly because of the size of the plant, the critical nature of its operations, the number of assets that must be checked or the lack of suitably trained personnel, then the next step is to take asset and machine health monitoring wholly online. In this case, data from permanently installed sensors, either hardwired back to junction boxes or connected wirelessly, will be routed via a secure internet connection to a centralised computer system that is capable of advanced data analysis.
Online systems are normally configured to provide either ‘surveillance’ or ‘protection’. Surveillance monitoring systems are often configured for periodic automated data collection using multi-channel, wireless or a series of single-channel transmitters. These can communicate directly with a PLC or DCS for trending, alarm and machine shutdown purposes. Protection systems, on the other hand, offer real time remote monitoring of critical machinery to provide a rapid and safe machine shutdown rather than periodic data collection.
Whatever the nature of a business, if it includes a manufacturing or process element, then the wellbeing of its mechanical assets becomes a prime concern. Steps must be taken to maintain the upkeep and reliability of those assets. Condition-based monitoring offers a well-proven route to achieving this goal; choosing a level of care – basic, better or best – that is most appropriate will ensure that assets deliver on their investment while operating more dependably and profitably.
For further information please visit: www.skf.com
