Predictive Maintenance in Manufacturing: Explanation & Examples

Ofir Levi

Ofir Levi, CoreTigo Head of Support

| 2 October, 2023
Discover how IO-Link Wireless-based predictive maintenance increases overall equipment effectiveness (OEE), minimizes downtime, reduces maintenance costs, and extends the lifespan of machines and equipment
Ofir Levi
By leveraging IO-Link Wireless-enabled real-time data collection, followed by professional analysis - manufacturers can not only predict failures but also optimize their maintenance practices.

Ofir Levi

Head of Support

In the fast-paced world of manufacturing, where manufacturing effectiveness and efficiency are key to success, unplanned downtime can wreak havoc on production schedules, costs, and overall operations. These make the ability to foresee when a machine might fail and take preventive measurements before it causes disruption a must-have. Predictive Maintenance in manufacturing is set to prevent this, and IO-Link Wireless is revolutionizing how industries approach machine upkeep. In this blog post, I’ll cover what predictive maintenance is, explore some relatable examples, discuss its benefits, and provide insights on implementation.

What is Predictive Maintenance?

Predictive maintenance using IO-Link Wireless is a proactive and thorough maintenance strategy that leverages the ability to collect data from anywhere, anytime in the factory, and perform analytics to predict when equipment or machinery is likely to fail or decrease in performance. Unlike traditional maintenance approaches that rely on predefined schedules, regardless of the actual condition of the machine, or reactive responses to breakdowns, predictive maintenance uses real-time data to determine the optimal time for maintenance activities. By doing so, IO-Link Wireless-based predictive maintenance increases overall equipment effectiveness (OEE), minimizes downtime, reduces maintenance costs, and extends the lifespan of machines and equipment.

Predictive Maintenance Examples:

To truly appreciate the transformative abilities offered by IO-Link Wireless to predictive maintenance in manufacturing, let’s explore some condition monitoring use case examples that demonstrate its diverse applications.

Predictive Maintenance in Metalworks and Automotive Manufacturing:

In the highly competitive metalworks and automotive manufacturing industry, precision, agility, and reliability are non-negotiable. Predictive maintenance is instrumental in maintaining the seamless operation of automotive assembly lines and ensuring the consistent flow of manufacturing processes. An example of such a case may be found in CNC tooling machines – responsible for grinding, drilling or milling metal and plastic components. These systems perform repetitive tasks with great precision, yet over time, wear and tear can take a toll on their performance and quality. IO-Link Wireless based predictive maintenance directly at the tooling point, powered by wireless sensor data and analytics, monitors these machines in real-time.
As sensors detect minor deviations in force, positions, vibrations, or even temperature, the system can pinpoint potential issues before they escalate into manufacturing deviations. Suppose a CNC machine tool begins to exhibit abnormal vibrations that could compromise its precision. Predictive maintenance algorithms can identify this anomaly, triggering an industrial automation function to schedule maintenance during a planned downtime window. This proactive approach ensures that the tool receives the necessary adjustments, optimization, or part replacements precisely when needed, preventing disruptions to the assembly line, and maintaining the consistency and quality of the manufacturing process.

Predictive Maintenance in Food & Beverage Processing:

In the food and beverage processing industry, where hygiene and product quality are paramount, predictive maintenance takes on a critical role. Food and beverage processing plants rely on a complex web of equipment and machinery, including ovens, refrigeration units, conveying systems (including transport track systems), and packaging machines, to ensure the hygiene and quality of consumable products. Any disruption in these systems can lead to costly product spoilage, downtime, and even potential health hazards to consumers.
IO-Link Wireless enhanced predictive maintenance in food and beverage processing focuses on monitoring the vital parameters of these machines in real time, anywhere in the plant. For instance, consider a large industrial oven responsible for baking food products. Continuous monitoring of temperature, humidity levels, and motor performance can reveal deviations that might indicate an impending issue. A sudden rise or fall in temperature could suggest a malfunctioning heating element or a clogged ventilation system, leading to a faulty product. IO-Link Wireless is the only wireless protocol that was originally designed for such applications, and IO-Link Wireless Devices are able to withstand the extreme physical conditions required in the factory.
Predictive maintenance algorithms analyze this data, detecting anomalies and issuing alerts to maintenance teams. By proactively addressing the issue, maintenance professionals can schedule maintenance during a planned production break, preventing downtime and ensuring that the oven operates within the precise temperature and humidity ranges required for consistent and safe food and beverage processing.

Predictive Maintenance in Consumer Packaged Goods (CPG):

In the world of consumer-packaged goods (CPG), where swift and seamless production is essential for delivering consistent product quality, predictive maintenance has emerged as a vital component in ensuring operational excellence. In CPG facilities the accuracy and reliability of the production line are essential as hygiene, air-tight packaging, and other elements must be strictly kept. IO-Link Wireless allows predictive maintenance technologies to extend their capabilities to these environments.
In such a case as a CPG facility responsible for filling and packaging hygiene products, the filling machines require constant precision and flawless operation. Through the implementation of predictive maintenance, a network of IO-Link Wireless connected sensors continuously monitors these machines. They monitor variables like pressure, temperature, and airflow, seeking any subtle deviations that might signal potential issues.
Such detections enable predictive maintenance and IIoT platforms to identify various issues and notify the maintenance team. By doing so, CPG facilities ensure that their packaging lines remain efficient, avoiding hazardous malfunctions. This strict attention to equipment health not only sustains operational efficiency but also safeguards the quality and consistency of the consumer packaged goods delivered to the market. In essence, predictive maintenance in the CPG industry not only maintains the integrity of production lines but also assures consumers of consistently high-quality products. It exemplifies how versatile and indispensable predictive maintenance has become in addressing the unique needs of diverse industries, and how critical it is to be able to monitor these variables anywhere and anytime with the help of IO-Link Wireless communication.

By expanding on these industrial realm examples, we gain a deeper understanding of how IO-Link Wireless-based predictive maintenance strategies are tailored to the unique needs of various industries. Whether in automotive manufacturing, food & and beverage processing, consumer packaged goods, or any other sector, wireless cross-plant predictive maintenance proves to be a versatile and indispensable tool for optimizing operations, enhancing quality, and minimizing costly downtime.

Benefits of Predictive Maintenance in Manufacturing

1. Predict Failures

One of the primary benefits of predictive maintenance is the ability to predict when equipment failures are likely to occur. By wirelessly monitoring the condition of machinery in real time, manufacturers can identify potential issues before they lead to catastrophic breakdowns. Since IO-Link Devices often cannot reach anywhere in the factory or on the machine, the use of IO-Link Wireless connectivity is required and enables spotting these potential failures before they happen.

2. Lower Maintenance Costs

IO-Link Wireless enhanced predictive maintenance enables cost-effective maintenance. Instead of performing routine maintenance at fixed intervals, manufacturers, engineering teams, and managers can allocate resources where they are actually needed, reducing unnecessary expenditures on equipment that is still in good condition.

3. Reduced Downtime

Unplanned downtime can be a production nightmare, causing schedule disruptions and putting the profitability of the manufacturing plant at risk. Predictive maintenance empowered by IO-Link Wireless helps manufacturers avoid such scenarios by detecting potential setbacks, and scheduling maintenance during planned downtime, minimizing disruptions to operations and production.

4. Safer Working Conditions

Making sure that the equipment is in proper working condition enhances workplace safety, as faulty equipment might lead to dangerous and hazardous situations, putting human safety at risk. Utilizing IO-Link Wireless for predictive maintenance reduces the risk of accidents associated with equipment failures, creating a safer working environment. Knowing the equipment is in constant tracking and is monitored for their safety, may also put workers’ minds at ease, allowing them to focus on work and achieve better results.

5. Improved OEE (Overall Equipment Efficiency)

Manufacturers are able to enhance OEE by optimizing machine performance, thus increasing productivity. Real-time data collection via IO-Link Wireless, used for predictive analytics enables better decision-making, leading to higher equipment utilization and efficiency. Keeping an eye out on every piece of equipment enables this and ensures no damaged part causes the entire process to be performed in a non-optimal manner.

Predictive Maintenance vs. Preventive Maintenance

In manufacturing, it’s important to distinguish predictive maintenance from preventive maintenance, as the two are different in nature.
Preventive maintenance is based on predefined schedules, where maintenance activities are carried out at regular intervals, on pre-defined timeslots, regardless of the actual condition of the equipment. While it can prevent some failures, it may lead to unnecessary maintenance and higher costs, as the entire system is being checked and working parts may be replaced for no actual reason. As in this method, the different parts of the system are not being monitored constantly, and in real-time, it’s still subjected to malfunctions and fails, as even new parts may be faulty and cause production issues.
Predictive maintenance, on the other hand, is data-driven and tailored to the specific needs of each piece of equipment. It aims to maximize the use of equipment while minimizing maintenance costs and downtime. By connecting the different parts of the machine using IO-Link Wireless, manufacturers ensure the full and real-time view of the entire system of the machine. This results in smart maintenance, which relies on replacing or fixing parts that actually need to be attended to, and not simply attending to everything blindly.

How to Implement Predictive Maintenance in Your Manufacturing Process

Implementing predictive maintenance in manufacturing in an optimal and beneficial manner requires careful planning and the right tools. For these to happen, several key functions need to be taken:

1. Data Collection: Collecting relevant data from your equipment is fundamental for predictive maintenance, as the data is the cornerstone of this operation. This can include sensor data collected via IO-Link Wireless, performance metrics, and historical maintenance records.

2. Data Analysis: Using advanced analytics and machine learning algorithms is key to analyzing the data and identifying patterns or anomalies that indicate potential issues. Knowing how to handle and process the data is as important as collecting it, and without such smart analysis, the raw data itself has very little value.

3. Predictive Models: An additional important step in implementing predictive maintenance is developing predictive models that can forecast equipment failures based on the data analysis. These models should consider various factors, such as temperature, vibration, changes in performance, usage patterns, and abnormalities.

4. Integration: Integration of predictive maintenance solutions with one’s existing systems, is the next step in the process. This includes manufacturing execution systems (MES), supervisory control and data acquisition (SCADA), and other relevant software. The integration must be done in a professional and precise manner, so it itself will not cause harm to the smooth operation of the machinery, or cause any faults. It also needs to be done in such a way that the monitoring equipment itself works in the most efficient manner. For example, by connecting several sensors into one IO-Link Wireless Hub, instead of having separate IO-Link Wireless devices for each, or integrating industrial wireless connectivity capabilities directly into a device, using a suitable IO-Link Wireless device.

5. Monitoring: Continuously monitoring the condition of the equipment in real-time will ensure the best results and swift handling in case of a malfunction or wear and tear. This will also make it possible to compare past data to current one to see if anything changes over time. Implementing alerts and notifications to inform maintenance teams of impending issues is a complementary part of the monitoring, and is the practice that puts the data into proper use.

6. Maintenance Planning: Once data is continuously and methodically collected and put to use, it is also important to plan maintenance according to it. Using the insights from predictive maintenance to plan maintenance activities more efficiently is the core of predictive maintenance. Scheduling maintenance during planned downtime or when the equipment is not in high demand results in greater efficiency, which leads to better profits.

7. Feedback Loop: As the entire predictive maintenance process is completed and implemented within a manufacturing facility it is important to remember that these settings are not eternal. Continued refinement of the predictive maintenance models based on new data collected and feedback from maintenance activities will generate new action items to be handled and will indicate reconfigurations to be done over time. This might be referred to as “The predictive maintenance of predictive maintenance”, whereas industrial professionals must stay vigilant and make sure our monitoring process stays optimal. In this manner, our predictions become more accurate over time.

Machine-centric platforms for Data Collection and Machine Optimization, such as TigoLeap allow manufacturers to complete this process, including the perpetuated self-optimization entailed in it. Being able to use a single source for all these activities gives manufacturers full control over their machines, rather than being subjected to unexpected and ad-hock fixes and maintenance.

Predictive Maintenance and IO-Link Wireless: Conclusion

Predictive maintenance in manufacturing is a transformative approach that leads to substantial cost savings, improved efficiency, and enhanced safety. By leveraging IO-Link Wireless-enabled real-time data collection, followed by professional analysis – manufacturers can not only predict failures but also optimize their maintenance practices. These ultimately lead to enhanced machine capabilities, processes, and manufacturing, increasing OEE, and decreasing unplanned downtime and damage caused by insufficient data or insights.
Predictive maintenance solutions must be tailored for each use specifically and cannot simply be copied from one facility to another – as many variables change and each requires its own set of specific configurations. Ensuring that manufacturing processes run smoothly and efficiently, eventually allows boosting a manufacturer’s competitive edge in the industry, as well as preventing unnecessary damage caused to the machinery and or production lines.
Predictive maintenance is not a trend in the industrial world, but rather a necessity of reality. It’s a smart strategy that empowers manufacturers to stay ahead of the curve, anticipate challenges, and keep production lines working with precision and reliability.
By harnessing the abilities of IO-Link Wireless technology, manufacturers can access data in a way never before feasible – anytime and anywhere in the factory. This sheds new light on entire operations, providing a full view – leading to full control.

Ofir is an experienced support manager with 23+ years of experience in global tech companies and industrial automation. He possesses strong skills in process control, industrial communication, and control systems. As Head of Technical Support, Ofir led teams of technical engineers providing presales, post-sales, and professional services at Unitronics and Megason. Ofir holds a BSc. in computer science and electronics & control.