Predicting the end of unplanned downtime

Any unscheduled downtime will cause a major headache for engineers in the food and beverage manufacturing sector, but predictive maintenance can be a remedy.

By John Rowley October 17, 2019

With food manufacturers being continually squeezed on price by retailers and asked to fulfill orders for supply that can seem, at best, challenging, improving productivity is a priority. Tight timescales mean many lines are already running on a near 24/7 basis, leaving little leeway even for scheduled maintenance, let alone an unexpected breakdown. This can lead to overcautious service and maintenance regimes, which are expensive to support, but are preferable to unscheduled downtime.

Most production line failures are not characterized by a sudden fault that results in immediate line stoppage. It is often a gradual degradation that impacts on product output. Before the line eventually stops, it might have spent a considerable period producing inconsistent goods – that add to the bottom-line cost of the issue, due to waste. So both unscheduled downtime and the developing causes of that downtime will directly impact productivity.

The good news is that random equipment failure does not have to be a fact of life. Modern condition monitoring sensor technology can be retrofitted to rotating plant and equipment, while many of today’s plant and machine controllers will have monitoring and diagnostics functions built in, ready to use.

Taking advantage of these technologies can take companies into the realm of predictive maintenance, where advanced warning of impending equipment failure gives engineers enough time to plan repairs during scheduled maintenance periods.

A leap forwards

A conceptual and technological leap forwards from preventative maintenance, intelligent predictive maintenance ensures an asset is serviced only when needed. Predictive maintenance spots equipment problems as they emerge and develop, giving a warning of impending failure and so helping to maximize asset availability.

Importantly, these predictive maintenance solutions are not complex; frequently they are simple and cost-effective to implement, and often they can be built from functions that already exist within the plant’s control equipment.

Take, for example, the add-on sensors that have been developed to monitor the increases in operating temperature, excessive current draw, changes in vibration characteristics and significant shifts in other operating parameters that can all be indicative of impending problems in rotating machines. Today these sensors come with embedded ‘smart’ functionality, revolutionizing condition monitoring.

A simple add-on to pumps, motors, gearboxes, fans and more, these sensors used a simple traffic light system of red, amber and green lights to provide at-a-glance monitoring of the condition of the machine. They can also be connected into wider factory automation networks using Ethernet and a managing programmable logic controller (PLC) for a smarter solution.

From traffic lights to telemetry

In isolation, sensors can offer a starting point to implementing preventative maintenance strategies, but there are limitations to the traffic light warning system. While it indicates that a problem is developing, it gives no real clue as to what the problem might be or how serious it is; it offers no practical recommendations as to how the problem should be addressed; and while it shows problems developing on individual machines, it fails to provide an overview on the asset health of the plant.

A smart condition monitoring (SCM) kit can provide an integrated approach to monitoring the condition of individual assets and enables a holistic approach to be taken to monitoring the asset health of the whole plant. Individual sensors retain the traffic light system for local warning indication at the machine, but at the same time information from multiple sensors is transferred over Ethernet to a PLC for in-depth monitoring and more detailed analysis.

The SCM kit provides a plug-and-play solution for machine condition monitoring. Sensors can be added to machines as and where required, with a teach function allowing the sensor and controller to learn the normal operating state of the machine, generating a memory map of key parameters. Once set up, the SCM provides 24/7 monitoring of each asset, with functions including bearing defect detection, imbalance detection, misalignment detection, temperature measurement, cavitation detection, phase failure recognition and resonance frequency detection.

Linking multiple sensors into the control system enables the controller to analyze patterns of operation that are outside the norm, with a series of alarm conditions that can provide alerts when attention is needed. The SCM analysis provides detailed diagnostics, offers suggestions for where additional measurements should be taken, and provides maintenance staff with precise error identification. It can even make recommendations as to what rectification actions should be taken, with clear text messages presented to personnel. This information can also be networked to higher-level systems for ongoing trend analysis across all the assets around the plant.

A practical example

Muntons Malt, a producer of malted barley, is reaping the benefits of this solution to protect fans and motors vital to its large-scale and sensitive production process. The operation team had experienced issues with difficult-to-reach bearings inside a large fan housing, realizing too late that a problem existed, and was forced to make an unscheduled stop to one of the lines to make repairs.

Determined to learn from this, the company installed the smart condition monitoring system on two large 315 kW fan sets and a single 90 kW fan set, referencing the electric motor, power transmission coupling and main fan shaft bearing on each. The company is now aware of the health of the fan sets and has a clear picture of any necessary maintenance way in advance of needing to make physical changes. Remote monitoring and fast diagnosis of any issues has also made the company very responsive should the operating parameters that have been set, even be approached.

Live information and any alarms are displayed on a HMI mounted in the control enclosure and the system can work autonomously of any other automation, with multiple sensors located and recognized by unique IP addresses. However, at Muntons Malt the visual information as well as the alerts were connected into the existing automation software platform.

This ease of connectivity illustrates further benefits of today’s condition monitoring technologies, which can provide immediate, visible alarms anywhere in the world on smart devices. For multi-site businesses, this can aid in quickly changing over production schedules from one plant to another to fulfill the most pressing orders or can alert remote maintenance teams of the need to perform more detailed diagnostics.

This information isn’t just coming from external sensors. Modern drives, PLCs, supervisory control and data acquisition (SCADA) systems and other automation products have comprehensive diagnostics capabilities inbuilt, monitoring not only their internal workings but also parameters such as current draw, voltage and temperature in connected motors, pumps, fans and compressors. All of this helps to build a detailed picture of the health of plant assets.

With a simple plant network backbone, this information can be shared around the plant and beyond. Indeed, this sort of functionality is a key aspect of Industry 4.0 and is at the heart of the benefits of the digitalization of production.

Predictive maintenance strategies can offer comprehensive analysis on the health of individual machines as well as a holistic overview on the health of the wider plant. The result is vastly improved scheduled maintenance and optimized asset lifecycle management. With maintenance able to be planned in-advance, there is far less unscheduled downtime and significant reductions in the loss of service at short notice. Also when assets are serviced only when needed, food and beverage producers can benefit from increased productivity and efficiency, with a very real impact on the bottom line.

This article originally appeared on the Control Engineering Europe website. Edited by Chris Vavra, production editor, Control Engineering, CFE Media,

Author Bio: John Rowley is the food and beverage sector specialist at Mitsubishi Electric.