Predictive analytics is something of a buzzword in the process manufacturing industry, with plants racing to adopt or extend predictive analytics tools like predictive maintenance and predictive monitoring. 

Although it’s widely known that predictive monitoring is effective at boosting productivity, manufacturing companies don’t always leverage its full value for equipment maintenance as part of boosting overall equipment effectiveness (OEE). Yet predictive monitoring can be highly impactful in lowering maintenance costs, extending equipment lifecycle, and reducing unexpected downtime. 

What is predictive monitoring?

Predictive monitoring is a form of predictive analytics which monitors conditions across the entire plant. It uses artificial intelligence (AI) and machine learning (ML) to analyze enormous datasets that are gathered from Industrial Internet of Things (IIoT) devices, sensors, and other plant data sources. These data sets are far too large for human analysis, but ML analytics can rapidly scan them to identify patterns and themes within the data. 

Predictive monitoring solutions learn what “normal” operating conditions look like so they can spot any anomalies. When they notice a significant change in the plant or any of the equipment within it, they generate an alert which guides maintenance engineers to faster root cause analysis and resolution. 

How does predictive monitoring make equipment maintenance more efficient?

No issue goes unnoticed

AI solutions can pick up on the earliest signs of equipment failures, many of which are not evident to the human senses, like small changes in sound levels, altered vibration patterns of machinery, or slight variations in product quality or quantity. While highly trained and experienced employees often “sense” that something isn’t right in the plant, these hunches are too nebulous to stop production while an investigation is carried out, and too vague to guide maintenance teams towards a specific item of equipment. 

Additionally, AI-powered predictive monitoring is more reliable and consistent than human monitoring. Even the best employees can have a bad day and miss the signs of a nascent incident, but that can’t happen with predictive monitoring. AI tools don’t get sick, tired, or overwhelmed, and they don’t make mistakes or second-guess their own conclusions, making them more accurate and more reliable for equipment monitoring. 

Equipment lifetime is extended

Predictive monitoring produces the earliest possible warnings about potential equipment failure, allowing plenty of time for plant engineers to investigate the situation and maintenance teams to carry out the fix, before the issue snowballs to the extent that repair is no longer possible. 

For example, a worn-out ball bearing is relatively quick and cheap to place, but if left unnoticed for too long it can result in damage to the machinery, and then the only option might be to replace the entire piece of equipment. 

Maintenance scheduling is more accurate

In addition to providing early alerts, predictive monitoring solutions deliver considerably better visibility into conditions within the plant, which helps prevent companies from being taken by surprise by unexpected issues. This way, maintenance teams can gather the parts they need and ensure that engineers with specific skills are available before they schedule downtime, preventing a situation where production is paused and maintenance teams arrive, only to discover that they aren’t able to complete the repair today and will have to come back and stop production another day. 

Equipment repair is less expensive

Maintenance costs are a significant expense for manufacturing companies, and as fuel prices rise, plants need to cut costs whenever they can. Early alerts typically mean that the fix is shorter, easier, and requires fewer expensive parts, which helps keep expenses down. Predictive monitoring also enables better maintenance scheduling, so that maintenance teams can be sent in when they are most needed and will deliver the greatest return on maintenance input. 

Root cause analysis is more quick and accurate

When a predictive monitoring solution gathers and analyzes data from across the entire plant, it can provide more detailed information that speeds up root causes analysis and makes the repair more effective and accurate. Some solutions produce a comprehensive report about plant conditions which helps guide engineers to investigate the relevant parts or processes. 

Plant disruption is kept to a minimum

Every plant has to balance the costs of delaying maintenance against the costs of lost production while maintenance is carried out. Early alerts give plant engineers enough time to decide when to best schedule the maintenance for minimum disruption. Often a number of repairs can be scheduled together, because the warning was early enough that a delay of a few days won’t significantly affect equipment or production. 

Decisions can be made from a position of strength

Sometimes plant engineers need to make hard choices between a quick patch that minimizes disruption to production now, but will require equipment to be replaced sooner, and halting production for a thorough fix that extends equipment lifetime. Early and detailed alerts from some predictive monitoring solutions give plant engineers enough time and information to make considered decisions about equipment maintenance. This allows engineers to reflect and make changes proactively, instead of reacting out of stress. 

How should you apply predictive monitoring to have the biggest effect on equipment maintenance?

As with most business decisions, the first step is to set goals. Process manufacturing plants may have many goals when they implement predictive monitoring, including lowering plant costs, reducing waste, and cutting downtime, so you’ll need to elevate equipment maintenance as your primary concern for predictive monitoring and select KPIs that move you towards improved equipment maintenance efficiency. These might include monthly maintenance costs, part failure frequency, or unexpected downtime. 

Predictive monitoring is most effective when you roll it out over the entire plant. There’s often a temptation to monitor only the most expensive or hard-to-replace equipment, but low-cost items like valves can ratchet up maintenance costs and lost production hours if they stop working properly or fail entirely. 

You’ll see the biggest impact on equipment maintenance when you can access real time or near time plant data. This is leading many plants to implement faster data sharing through edge computing networks and 5G connections which can cope with quickly sending enormous datasets from IIoT devices to your predictive monitoring solution. 

Last but not least, predictive monitoring can only have an effect on equipment maintenance when it’s part of a broader adoption of a digital culture. Your employees need to trust the insights that they receive from AI-powered predictive monitoring tools so that they’ll follow up on the information and won’t ignore alerts or recommendations. 

Predictive monitoring can boost efficiency for equipment maintenance 

Improving efficiency for equipment maintenance is an important use case for predictive monitoring. By setting clear goals and KPIs, enabling real time data transfer, and rolling out predictive monitoring across the entire plant as part of a shift towards digital culture, manufacturing companies can lower costs, speed up repairs, enhance decision-making, and minimize disruption in the course of boosting equipment maintenance efficiency.