Get to know “predictive maintenance”
By Dan Anderson, Product Manager-Components, Omron AutomationAutomation General Machine Building noads
Sponsored by Omron
How the maintenance strategy leverages innovations in sensing and analytics technologies to overcome the drawbacks of traditional preventive maintenance.
Traditional preventive maintenance methods take place at scheduled intervals and require skilled workers to complete time-consuming manual processes. Problems arising between inspection dates may go unnoticed, prompting companies to replace equipment prematurely to avoid failure.
The case for adopting a predictive maintenance strategy
The manufacturing industry first started out with what is now termed “reactive maintenance”: a “strategy” in which machines would be run until they failed, and subsequently repaired or replaced. Given the serious disruptions that often resulted from machine failure, equipment manufacturers began to recommend that their machines be inspected and serviced on regular, time-based intervals. This is now known as “preventive maintenance.”
However, even preventive maintenance has some downsides. Since it is not a continuous process — rather, maintenance checks occur at specified intervals — and it can be quite time-consuming and expensive to check all equipment, there is a significant possibility that signs of impending failure could be missed. To stay abreast of any unrecognized signs of deterioration, manufacturers often opt for replacing equipment before it becomes truly necessary. This, for obvious reasons, increases the equipment’s total cost of ownership.
To determine the exact time that equipment needs to be repaired or replaced, yet another strategy is needed — one that involves continuous monitoring. Thanks to technological advancements, such round-the-clock monitoring is in fact possible, and it does not require a person to do any manual checkups.
Dubbed “predictive maintenance,” or “PdM” for short, this proactive strategy uses real-time data to identify component failures early, reduce unplanned downtime, and avoid costly repairs. Advances in sensing, analytics, and communications technologies are making PdM increasingly practical and affordable for small, medium, and large companies.
Benefits of predictive maintenance
The primary objective of PdM is to prevent unplanned equipment failure during manufacturing hours, also known as unplanned downtime. Unplanned downtime is a critical cause of production delay and profit loss. It means that the equipment essential for manufacturing your product is unable to produce its intended output at a time when your labor resources are onsite and expecting the machines to be up and running.
This reduces your company’s overall manufacturing efficiency and profitability. In many cases, if equipment fails during a manufacturing process, that “work-in-process” product must often be scrapped due to not meeting quality requirements.
Predictive maintenance aims to automate the data measurement and analysis process on equipment. Typically, this is a manual process where skilled labor resources must go from machine to machine to take equipment readings over time.
These readings form a trend, which must then be analyzed to determine equipment health, and a failure point is interpolated or estimated. Currently, this is a very manual process where you are paying skilled labor — which is in limited supply — to perform this task. A PdM strategy automates this process into a “go/no-go” output which notifies the user when equipment needs service.
Taking these above points into account in an environment with stressed supply chains, oftentimes equipment or replacement parts that were previously stocked or readily available are no longer available with the lead times we plan for. Having the ability to predict future failure gives additional lead time for ordering replacements, further reducing the chance of unplanned downtime.
What you need to implement PdM on existing manufacturing systems or a new build
In every case, a PdM solution will involve some type of sensor and analyzer/monitor unit. Naturally, this will be specific to what type of equipment you are looking to implement a PdM strategy on. In addition, the user will need to determine if they are looking to implement PdM on a single equipment type (i.e., ISR on servo motors) or across an entire manufacturing floor and potentially outsourcing that monitoring to a third party. The latter requires an integrated software solution that accepts multiple data input types.
Many solutions today offer a variety of communication output types, oftentimes connecting to a PLC or remote location that would consolidate that monitoring. This means that you would also need your networking infrastructure within the manufacturing facility to accommodate Ethernet communication.
What conditions do predictive maintenance solutions monitor?
The types of issues that PdM solutions can successfully monitor is limited to what is offered by suppliers in the industry. Currently, the industry offers solutions for multiple failure modes in three-phase motor motoring (vibration/temperature, current abnormality, ISR), power supply condition monitoring, insulation resistance condition monitoring, thermal or infrared condition monitoring for environments where heat is critical, and heater condition through resistance trend monitoring. The industry is producing new solutions every year for different equipment types as the trend progresses, with valves being a likely focus area.
What does PdM data analysis reveal?
Typically, the primary output of most solutions is to notify the user when service is needed. In certain applications, the output will offer specific information as to where the potential failure is going to occur. A good example of this would be thermal/infrared condition monitoring, where an infrared sensor looks at an area of interest (like a control panel) to detect elevated temperatures in a particular area within that control panel.
This lets the customer pinpoint which control panel component is in the beginning stages of failure and replace it before failure occurs. When monitoring multiple pieces of the same equipment, like servo motors, PdM can also to determine which servo motor is failing the ISR test, allowing the user to determine its location.
Getting started with predictive maintenance
When implementing PdM for the first time, an initial consideration is to determine whether you want to implement an ad-hoc solution (equipment by equipment) or whether you would prefer a comprehensive PdM solution of all manufacturing equipment. There are two pieces of the puzzle to consider: hardware and software.
The hardware component will be the sensors and analyzers/monitors that receive raw data from the piece of equipment and convert it into a go/no-go output. The software component is typically a data aggregator that combines inputs from multiple hardware sources onto a customer-specific graphical overlay. Some of these software solutions accept raw sensor inputs and perform condition analysis in the cloud, returning the “go/no-go” output after analysis has been completed.
Customers should determine whether they are looking to implement PdM over time, in which case they would be spreading out the costs and implementation, or if they are more interested in tackling it as a larger project all at once. It basically comes down to the resources they can dedicate towards the project and their desired implementation timeline.
Predictive maintenance minimizes the likelihood of unplanned equipment failure during manufacturing hours by automating the data measurement and analysis process on equipment. Instead of relying on skilled labor resources that go from machine to machine, manufacturers using PdM benefit from round-the-clock remote monitoring that identifies trends and signs of impending failure automatically. By predicting future failure, PdM solutions provide additional lead time for replacement parts (or entire machines) to be ordered before failure occurs.