The Complete Guide to Predictive Maintenance in Manufacturing

In manufacturing, keeping machines running at peak efficiency and minimizing downtime has a major impact on the company’s overall costs and output. Predictive maintenance (PdM) can help manage and optimize maintenance tasks in real-time to ensure equipment is maintained at the optimal times.

While maintenance is vital to preventing costly equipment breakdowns, running maintenance early or too often can increase costs and downtime. Predictive maintenance can help manufacturers find the right balance to extend the life of their equipment while increasing overall efficiency.

If you’re considering implementing PdM into your manufacturing, this article will provide you with an overview of the benefits of predictive maintenance in manufacturing, what is involved in implementation, and discuss recent examples and advancements to help you get started.

What is Predictive Maintenance?

Predictive maintenance is a proactive approach to maintenance that aims to detect and solve issues with equipment performance before they occur. The strategy involves collecting data from sensors to constantly monitor and analyze equipment conditions and then make predictions about its future performance.

PdM was first applied in the manufacturing industry in the 1990s and has gained more traction in recent years due to the introduction of the Industrial Internet of Things (IoT), machine learning, big data, and cloud computing. These technologies provide more data points using more affordable sensors and the growth in machine learning and cloud computing means predictive models are more accessible and affordable to manufacturers.

In a recent survey of manufacturing industry experts, 80% agreed that predictive maintenance is essential for the manufacturing industry and will gain additional strength in the future.

To understand where predictive maintenance fits, here are the current most common maintenance strategies in manufacturing:

• Reactive Maintenance: Equipment isn’t fixed until it is broken. This results in more unplanned maintenance and downtime in an effort to reduce upkeep costs.
• Preventative Maintenance: Maintenance activities are scheduled regularly to extend the life of the equipment. This approach can increase maintenance costs and doesn’t take into account the risk of components breaking down randomly.
• Predictive Maintenance: Uses AI or ML-based models to predict when machines will require maintenance and schedule tasks before components break down.

Benefits of Predictive Maintenance in Manufacturing

Predictive maintenance has countless benefits for manufacturing from a cost, performance, and safety perspective. Here are some of the most compelling benefits:

• Improved ROI: The return on investment for PdM can clearly be seen in maintenance and repair costs. According to a Saagi report, maintenance costs are reduced by 30% by avoiding unnecessary maintenance tasks. Repair costs are also lowered by sensor data which gives insight into the specific component or issue that needs attention. Also, predictive maintenance allows manufacturers to order new parts in advance, reducing the costs of waiting for parts or storing extra parts for unplanned maintenance.
• Increased Machine Lifespan: Predictive maintenance tracks asset performance to identify issues before they reach the stage of serious damage. This ensures manufacturers get the full life out of each component and only replace it right before it is at risk of causing damage to the machine or affecting overall performance.
• Better Performance and Less Waste: Better-maintained equipment runs more efficiently and produces less waste. PdM can identify when equipment is operating sub-optimally and causing inefficient use of raw materials, energy, machine time, or labor costs. According to the Saagi report, PdM leads to a 25% growth in productivity.
• Greater Operator Safety: Big data analysis helps eliminate safety risks and identify potentially dangerous conditions. Early warning alerts for faulty equipment can help prevent injuries and workplace accidents in manufacturing.
• Asset Protection: PdM provides key data for repairs and identifies abnormal behavior after a repair. This helps ensure that maintenance doesn’t negatively impact other parts of the machine.
• Improvement Opportunities: PdM involves keeping track of every element of the machinery and its performance, input, and output. This data can help identify new ways to optimize the equipment or the overall manufacturing process.

What Does Predictive Maintenance Involve?

A predictive maintenance development project will involve four basic parts. Here is an overview of what this project involves:

1. Install Condition Monitoring Sensors: The first step is to install sensors that gather real-time performance data and machine health info. IoT technology now allows connections between machines, software, and cloud technology.
2. Data Collection: Next, develop a pipeline that will collect data from all sensors and prepare it for further data analysis and processing.
3. Train Predictive ML Models: Combining historical data with real-time sensor data provides failure predictions that can be trained over time to become more accurate.
4. Analytics and Monitoring: Develop software for maintenance and management staff to review systems and events, track machine health, and schedule human-machine interactions.

One major decision to be made in any predictive maintenance development project is which approach to take to make predictions:

Rule-based Predictive Maintenance

This PdM approach collects data through condition monitoring systems and then sends alerts when specific rules have been activated. This rule-based AI system requires cross-department collaboration between product, engineering, and customer service to understand the direct and indirect causes of equipment breakdown.

Once the cause-and-effect relationships are understood, a virtual model can be created which outlines the behaviors and interdependencies between the different IoT elements. For example, if the temperature of Machine A increases above X degrees, send an alert.

This approach delivers some automation but mainly relies on the team’s understanding of which events to monitor and the correct response to specific alerts.

Machine Learning Predictive Maintenance

Machine learning algorithms can be built which take all the data collected from the sensors and work based on a probabilistic approach. Using data generated from IIoT sensors historically and in real-time, ML models can determine equipment’s normal behavior and automatically detect anomaly data and events.

The main benefit of machine learning for predictive maintenance is that it can find correlations the maintenance teams may have missed and they can dynamically adjust to new data and make sense of what’s happening in real time.

Recent Advancements in Predictive Maintenance

With the growth of technology like IIoT and machine learning, predictive maintenance has seen increased adoption. Even more recent advancements and trends are causing lots of hype around PdM and creating even more compelling reasons for manufacturers to consider the approach.

• Plug and Play Technology: Legacy equipment has always been a barrier to digital transformation in manufacturing. This is especially true for large companies where machinery isn’t equipped with connectivity to send real-time info. Plug and Play devices include ready-to-use computer equipment to connect legacy machines and make PdM available to manufacturers without needing to replace older equipment or systems. As the “plug and play” name suggests, these devices can also be installed with very little technical knowledge.
• Supply Chain Integrations: In addition to monitoring asset lifespan, predictive maintenance can be extended to the supply chain to monitor production schedules and choose the optimal time for maintenance based on the supply chain. Integrating the supply chain and PdM system can also improve the delivery of new parts for replacement.
• Thermography Checks: Machines that aren’t able to monitor temperature can now benefit from infrared sensors to detect wear and rusting of machinery invisible to the naked eye. Thermal imaging is another new thermography technique that converts temperature info into an image for further analysis.
• Digital Twin: This technology involves creating a detailed virtual version of the company which can be used to test processes and make plans for new equipment installations before introducing them to the physical plant. Combining digital twins with PdM can improve predictive models.
• Predictive Maintenance as a Service: Another advancement improving access of PdM to manufacturers is Pdm as a service. 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 improve their operation.

Predictive Maintenance Manufacturing Examples

Companies have been benefiting from PdM for decades, but the last few years have seen some incredible advancements and allowed manufacturers to see even greater results. Here are a few global manufacturers that have implemented predictive maintenance into their operations:

Frito-Lay

During its manager presentation at the Leading Reliability 2021 conference, Frito-Lay reported that predictive maintenance helped the company reduce planned downtime to 0.75% and unplanned downtime to 2.88%. 

In one example, the PepsiCo subsidiary used PdM to help prevent the failure of a PC combustion blower mower. Had the company not received early warnings due to PdM, the failure could have caused the shutdown of the entire department. Another example involves increased acid levels in oil samples which could have led to downtime for their entire Cheetos Puffs production.

Mondi

The packaging and paper manufacturer implemented predictive maintenance to avoid abnormal shutdowns of its plastic extruder machine. During the PAW Industry Virtual Conference, Mondi revealed that a single failure of this equipment could cost the company as much as €50,000 in cleanup and lost revenue. The manufacturer estimates that predictive maintenance has helped them save up to €80,000 in operating costs and waste generated by the machine.

Noranda Alumina

The alumina product manufacturing facility implemented predictive maintenance in 2019 and has since saved $900,000 in bearing purchases and reduced downtime. In terms of performance, the manufacturer’s grease completion rate improved from 67% in 2019 to 92% in 2021 thanks to PdM.

How to Get Started with Predictive Maintenance

The benefits of predictive maintenance for manufacturing are clear. The only question most manufacturers ask is whether PdM can be implemented into their current equipment and systems, and how long it will take to see a return on the investment. Thanks to recent advancements, these cost and technology barriers are much lower, and nearly every manufacturer can see PdM as a viable option.

If you’re looking to get started with predictive maintenance for your company, reach out to NineTwoThree. We work with manufacturers to build machine learning models and data strategies to make the most of PdM. Our team has a strong background in manufacturing and even holds an IoT sensor patent or two.