Operators of production equipment are all too familiar with the dilemma: How do you improve product quality and increase system availability while at the same time cutting back on maintenance costs? The answer appears to lie in advanced strategies like condition-based or predictive maintenance.
So far most companies have taken a reactive or preventive approach, both of which pose serious limitations in terms of how much they can be optimized. To achieve real long-term improvements in efficiency, new approaches are needed.
Cost and complexity have thus far deterred many companies from pursuing more advanced maintenance strategies. With the rise of intelligent sensors, however, these solutions have become much more affordable and easier to implement.
A closer look at the four most common types can help to highlight the advantages of advanced maintenance strategies.
An approach that is still frequently used is reactive maintenance. Machines or systems are only repaired in the event of a failure or defect. The result is often unexpected production outages, as well as severe damage to machinery and equipment.
With preventive maintenance, machines or plants are shut down at predefined intervals to check and/or replace components. This type of maintenance often leads to fully functional components being replaced before their full service life has been utilized. This results in high costs.
Modern instrumentation makes it possible to acquire, process and assess data related to the health of machinery and equipment – continuously and with a high degree of precision. This information helps identify potential sources of malfunction and damage in advance so that corrective action can be scheduled for a time when it is convenient.
Components provide value for nearly their entire useful life, even while preventing the majority of primary (and therefore also secondary) damage. For detailed information about implementation of condition-based maintenance, see VDI guideline 2888.
Whereas condition-based maintenance involves evaluation of isolated data, such as vibrations, predictive maintenance goes a step further. All available data is pooled together and processed automatically to make even more precise predictions about the remaining service life of components.
This is done using big data analytics, where hundreds or even thousands of parameters from a variety of sources are aggregated and evaluated. Data mining techniques are used to examine historical data to uncover previously unrecognized – and often very complex – correlations. In combination with manufacturer data and information from measuring transducers, it becomes possible to make very precise predictions about when components are likely to fail.
This knowledge can be used to make more informed maintenance decisions, such as deferring tasks of lower importance to allow for prompt replacement of components where failure is imminent. It also allows for identification of systematic error patterns and their root causes, so that machine parameters can be adjusted to reduce wear.
Despite the many advantages of modern maintenance strategies, it is still best to evaluate which approach is the best fit for each application. This evaluation needs to take into account both technical and economical considerations as well as any applicable legal and regulatory requirements.
Condition monitoring with APROL ConMon
Condition Monitoring Systems (CMS) monitor the health of machinery and equipment. This makes them the ideal basis for implementing condition-based and predictive maintenance. APROL ConMon from B&R makes it possible to acquire, process and assess relevant condition parameters and can be set up with minimal effort. It makes implementation of condition monitoring systems and plant asset management solutions considerably easier.
APROL ConMon comes preinstalled on an industrial PC and is immediately ready to use. In addition to the engineering and operator software, the system also includes a high-performance database with an SQL interface. It is based on the extremely stable SUSE Linux Enterprise Server operating system. All required data is archived.
Typically installed in the control cabinet without a monitor, the industrial PC is accessed over the network from workstation computers using a web browser or VNC client.
APROL ConMon is completely scalable and can be implemented on standalone machines or large plants with numerous production lines. APROL ConMon can easily be paired with APROL EnMon for energy monitoring and APROL PDA for process data acquisition. It can even be scaled up to a full-fledged process control system at any time without sacrificing any of the previously invested engineering work.