The Definitive Guide to Predictive Maintenance in Manufacturing

This is a go-to guide for Predictive Maintenance in manufacturing. Learn what it is, how it works, its trends, and real-life examples.

 

Can you tell the minimum amount of maintenance work that a manufacturer needs to complete to keep its machines running at peak efficiency and avoid unexpected and costly downtime?

It’s a tricky question, though we can answer it with the help of predictive maintenance. Predictive maintenance differs from other types of maintenance due to its potential to manage and optimize maintenance tasks in real-time. So, manufacturers can extend the useful life of the equipment while also getting rid of abrupt equipment breakdowns.

Still, to know whether it’s worth implementing PdM into manufacturing, you first need to understand predictive maintenance, how it works, its trends, and real-life examples. In this article, get most of your questions answered about predictive maintenance in manufacturing.

What is predictive maintenance?

Predictive maintenance (PdM) is a proactive maintenance strategy that aims at detecting and solving performance equipment issues before they actually occur. Based on the data collected from sensors, a PdM system constantly monitors and analyzes equipment conditions and makes predictions about its operation.

BTW, if you want to know more about predictive maintenance and how to turn this approach into a success story for your manufacturing company, you can go and read our White Paper on Predictive Maintenance in Manufacturing.

Predictive maintenance in manufacturing

The manufacturing industry is probably the biggest application for PdM. This approach emerged back in the 1990s although it hasn’t gained immense popularity until recently. This happened because of the introduction of the Industrial Internet of Things (IIoT), Machine Learning, Big Data, and Cloud Computing, which made PdM more available and affordable to manufacturers.

As manufacturers struggled to keep up with the roaring demand, they have tried different maintenance strategies. One of them was reactive maintenance, which embodies the mindset of “don’t fix it until it’s broken,” yet which ends with the increase in unplanned maintenance and downtime. Unlike reactive maintenance, preventative maintenance allows scheduled maintenance activities to perform before the problem has occurred. It increases the longevity of the equipment, but it’s also very costly. And it still doesn’t eliminate the risk of equipment or its components breaking down randomly.

Predictive maintenance seems a better solution to manufacturing companies due to its ability to minimize downtime and cut maintenance expenditure. Besides, tight deadlines and profit margins in today’s manufacturing sector made unplanned downtimes even more unwelcome.

The survey of 300 industry experts organized by Frenus GmbH Darbi College proved that 80% of the respondents consider PdM essential in the manufacturing industry. 77% of them also believe that PdM is a real need to stay competitive in today’s business environment. (See more detailed results of this questionnaire below.)

Questionnaire
Question: How do you agree to the following statements?

Benefits of predictive maintenance in manufacturing

The list of the benefits of PdM in manufacturing is exhaustive, so we’ll focus only on the most important:

  • Better ROI: In the case of PdM, your return on investment (ROI) is huge. First, you save on maintenance costs since you don’t perform regular maintenance tasks that may be unnecessary. Secondly, PdM solves the problem of unplanned downtime and reduces a planned one. Repairing tasks take less time because data scientists already know where to search for faulty components and why they’ve broken down. Thirdly, manufacturers can order new parts or machinery in advance. Thus, they don’t waste time waiting for them or spend extra on stocking in case of planned maintenance.

  • The high lifespan of machinery: Deloitte states that PdM increases machine uptime by 10 to 20%. First of all, the system is tracking the asset performance, so it will notice a problem before the equipment reaches the stage of serious damage. Secondly, in PdM, the useful life of machinery is long because the equipment or its parts work out all its possible time instead of being replaced regularly (as it happens in preventive maintenance).

  • Reduced waste: Manufacturers can also increase their bottom line revenue by reducing waste. Sub-optimal operation (when your equipment is on the verge of breaking down) usually leads to wasteful production, such as in raw materials, energy, machine time, and labor costs. PdM can warn businesses about this issue before it even happens.

  • Better performance: PdM reduces the time needed for repair as well as the frequency of repairs in general. Therefore, manufacturing organizations can operate more efficiently. Over some time, factory conditions should improve, while equipment should break down more rarely.

  • Improved operator safety: Getting early warning signals about faulty equipment can prevent injuries in manufacturing. Big data analysis helps to eliminate safety risks on daily basis, plus to identify potentially dangerous conditions by keeping track of machinery for longer periods.

  • Asset protection: While fixing an equipment problem, manufacturers can sometimes affect other parts of the machine. PdM can help you notice any abnormal behavior after a repair so you can solve it early.

  • Room for improvement: PdM also guarantees manufacturers new improvement opportunities. By keeping track of the machinery, you can uncover new ways of optimizing the equipment.

Predictive maintenance key figures

How does predictive maintenance work

Basically, we can say that any PdM system is composed of four parts:

  1. Installed condition-monitoring sensors that gather and send further real-time performance data as well as machine health info. It’s possible due to IoT technology that combines the connection between machines, software solutions, and cloud technology

  2. Data collection pipeline that helps with collecting data from sensors in a raw format for further data analysis and processing

  3. Predictive ML models that are trained on the historical data and fed with real-time sensor data to obtain failure predictions

  4. Analytics and monitoring software that helps with data analysis, system events monitoring, and scheduling human-machine interactions

Predictive maintenance white paper

In addition, there are two common approaches to predictive maintenance that manufacturing companies can choose from if they want to implement PdM:

Rule-based predictive maintenance

The idea of rule-based PdM, which is also known as condition monitoring systems, lies in relying on condition monitoring sensors. Those sensors continuously collect data about equipment and then send alerts when a specific rule has been activated, in line with predefined protocols.

A special feature of rule-based AI systems includes close cooperation between production teams, engineering, and customer service departments. This cross-department collaboration is necessary to understand the direct and indirect causes that eventually lead to equipment breakdown.

Once a manufacturer knows those causes, it can create a virtual model of its connected system where they outline the behaviors and inter-dependencies between its different IoT elements. As a result, if the temperature increases above the predefined level in a smart factory, then the system sends an alert message to the team.

From the mentioned above, rule-based PdM delivers some level of automation. However, it’s still too dependent on the team’s understanding of what components or environmental events to monitor.

Predictive maintenance with machine learning

A different approach is to build predictive maintenance in manufacturing based on machine learning algorithms. In this scenario, ML-based systems define their own set of rules proceeding from data inputs. They take the correct data and work based on a probabilistic approach.

Precisely, an ML model uses and learns from the data generated from IIoT sensors historically and in real-time. This way, the model knows the equipment’s normal behavior and is able to detect anomaly data and events. It also helps to find correlations and make predictions for the production team to take action timely to remove the potential defect.

Here’s one more great thing about ML-based predictive maintenance: the model can dynamically adjust to new data and make sense of what’s happening in real-time.

Don’t miss our posts on traditional ML techniques and deep learning approaches to understand how manufacturers can solve a PdM problem better.

Trends and the future

Like any other relatively new technology, predictive maintenance has caused lots of hype about its adoption. Still, there are a number of specific reasons why manufacturers are looking in the direction of PdM. Below read five trends of PdM in manufacturing:

1. Plug and Play technology

One of the bottlenecks for introducing advanced technologies in manufacturing, including a predictive maintenance approach, is its dependence on legacy equipment. This problem is especially relevant to large companies where much of the machinery is not equipped with connectivity to send real-time info.

PnP (Plug and Play) devices can be an optimal solution in this case. These include ready-to-use computer equipment that can connect legacy machines to computers. As a result, PdM becomes available to manufacturers, without the need to replace older equipment. Aside from this, PnP technology doesn’t require any specific knowledge to be installed, so anyone can do it.

2. Supply chain cooperation

Another popular trend is to extend predictive maintenance to supply chain management, which unlocks new benefits to manufacturing companies. For one thing, PdM won’t only monitor the asset lifespan but will consider production schedules and choose the most optimal time for maintenance activities. For another, the system can help manufacturers order new parts for replacement automatically.

3. Thermography checks

Thermograph

Since heat is one the earliest indicators of equipment issues, manufacturers are starting to use infrared PdM checks on temperature. This becomes possible with thermographic analysis that expects using infrared scanners to detect wear and rusting of machinery that remains invisible to the naked eye.

The recent addition to thermography techniques is thermal imaging, which, thanks to ML, allows complete checks on the temperature at a safe distance. Basically, it takes infrared temperature measurements and converts this info into an image.

4. Digital twin coupled with PdM strategy

A digital twin is becoming one more mainstream technology in the manufacturing industry, helping businesses to streamline their operations. If a manufacturer has a detailed virtual version of its company, they can test processes before their introduction, make plans about new equipment installation, and so on.

To make use of this technology to its fullest, you can combine digital twins with PdM. For example, Tech27 tells how a digital twin paired with PdM helped to save an oil and gas production plant as much as $360,000 due to predicting a potential plant outage. The two approaches make a perfect pair when there is a target to predict and when high-quality operational data are available.

5. PdM As A Service

Last but not least, PdM as a service is one more interesting trend that draws the attention of manufacturers. This hybrid approach combines the effort of a dedicated service team and software so to make PdM instantly accessible to manufacturers.

This approach is particularly valued by original equipment manufacturers. OEMs produce parts and equipment for other manufacturers while PdM as a service enables them to collect real-time data from their clients’ equipment and, thus, improve their operation.

Examples of Predictive Maintenance in manufacturing

Learning from peers is better than entering the uncharted waters of PdM without researching the practical experience of others. For this reason, let’s take a look at three global manufacturers that have already implemented PdM into their operations.

1. Frito-Lay snack food manufacturer

PepsiCo’s subsidiary Frito-Lay has a positive experience of introducing a robust program of PdM technologies. According to its manager’s presentation at the Leading Reliability 2021 conference, PdM helped the company reduce planned downtime to 0.75% and unplanned one to 2.88%. The manufacturer also improved its operations in a range of ways thanks to PdM.

Specifically, the technology warned and helped to prevent the failure of a PC combustion blower motor. If the company hadn’t responded as early as it did, this failure could have caused the shutdown of the entire potato chip department for an unknown amount of time.

One more example includes the early detection of increased acid levels in oil samples, which most likely warned about oil degradation. Unless it hadn’t been fixed, this problem could have led to the downtime in Cheetos Puffs production and low sales.

2. Mondi packaging & paper manufacturer

Another interesting case includes the achievements of Mondi manufacturer that produces paper and packaging products. The company has implemented PdM specifically to avoid abnormal shutdowns of its plastic extruder machine in Munich’s plant, Germany. A single failure of this equipment cost the manufacturer as much as €50,000 in cleanup and lost revenue, according to Rainer Muemmler, the speaker at PAW Industry Virtual Conference.

By estimations, the introduction of PdM allowed Mundi to save from €50,000 to 80,000 mainly due to reduced operating costs and less waste generated by the machine.

Image from Unsplash

3. Alumina manufacturing facility

Noranda Alumina LLC specializes in alumina product production. The manufacturer implemented PdM in 2019, and since then, the technology has helped the company save approximately $900,000 in bearing purchases as well as reduce the downtime, as this was mentioned at the Leading Reliability 2021 conference.

One more acquirement of this alumina refinery included the impressive growth in the grease completion rate from 67% in 2019 to 92% in 2021, which the manufacturer also attributes to its implementation of PdM.

And if you want to read more about the nuances of PdM implementation, here’s a Predictive Maintenance case study covering a PdM machine learning solution for hydraulic systems.

Getting started with predictive maintenance

If you read this far, you probably already wonder whether and how predictive maintenance could be useful to your manufacturing organization. To research this question more thoroughly as well as to get to know how to shift to PdM smoothly, you are welcome to read our Predictive Maintenance White Paper.

Optimizing a maintenance strategy is difficult but important for manufacturers. The most reliable way to do it is to implement predictive maintenance. PdM will help you define the best time to do maintenance activities, warn you about any asset problem and potential downtime, and improve the useful life of the equipment.

 

We at Intelliarts AI love to help companies to solve the challenges with data strategy design and implementation, so if you have any questions related to Predictive Maintenance in particular or other areas of Machine Learning — feel free to reach out.


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