Predictive Maintenance: An IIoT-Driven Evolution (Reekoh blog)
Written by Reekoh
Industry 4.0 and the Industrial Internet of Things (IIoT) are bringing together a series of revolutions disrupting the manufacturing landscape. From supply chain automation to manufacturing-as-a-service, these disruptions will forever change the way goods are produced.
Yet perhaps one of the most impactful changes of the Fourth Industrial Revolution is the logical evolution of a much older concept—predictive maintenance. IIoT offers a wide array of sensor packages, remote diagnostic tools, and analytics software that can make predictive maintenance more accurate and more timely than ever.
In this article, we explore how IIoT drives significant changes in how predictive maintenance is carried out, and why predictive maintenance is so important to your success.
What is Predictive Maintenance?
Predictive maintenance is the process of identifying equipment failure before it happens. It includes:
- analyzing an asset to predict when it will fail before it actually breaks down,
- scheduling routine preventative maintenance accordingly, and
- identifying potential risks to take steps to avoid them.
Predictive maintenance is a sharp contrast to reactive maintenance, which is the act of repairing and maintaining machinery after it has broken down. Relying on reactive maintenance results in significant downtime while machines are offline and being repaired. This is a serious problem for any business that relies on machinery, as every hour of downtime is estimated to cost manufacturers an average of $260,000 per hour.
However, predictive maintenance is not a new concept. Manufacturing firms have been using scientific methods to forecast when equipment will fail since the emergence of complex machinery.
One anecdote from Control Engineering suggests that, “The start of predictive maintenance (PdM) may have been when a mechanic first put his ear to the handle of a screwdriver, touched the other end to a machine, and pronounced that it sounded like a bearing was going bad.”
Today, predictive maintenance employs a variety of techniques and technologies, such as:
- Vibration monitoring, one of the original predictive maintenance techniques, involves detecting anomalous vibration frequencies and identifying potential faults based on vibration characteristics
- Oil analysis, which requires sampling engine oil and determining if maintenance or oil replacement is required
- Alignment sensing, which employs lasers or ultrasonic sensors to determine if moving parts are aligned
- Motor testing, which uses analysis of electrical characteristics such as current signature, impedance, and surge detection to identify faults
How Does IIoT Benefit Predictive Maintenance?
In the past, predictive maintenance has relied on discrete sensing tools or manual collection of on-device data to inform predictive analysis. However, with IIoT, every machine can be equipped with internal sensors that constantly monitor and report machine status. This allows for real-time data collection, analysis, and simulation to provide insights on equipment performance.
Because of IIoT, organizations now have unprecedented visibility into their assets’ performance, allowing them to better target maintenance activities. Automated, IIoT-driven predictive maintenance systems may use internal sensors to detect malfunctions in a machine’s operational status, such as vibration, temperature changes, or wear patterns on parts.
Such IIoT-driven predictive maintenance has tangible benefits in the factory that any competitive manufacturing business cannot overlook.
In fact, according to an extensive study by PwC, over 95% of respondents with pilot programs involving PdM 4.0—or the implementation of modern technologies such as IIoT in predictive maintenance—95% have achieved results as a result of the switchover to modern predictive maintenance techniques.
1. Reduced costs
In the PwC study, respondents experienced a 12% reduction in costs due to IoT-based predictive maintenance implementations. Advanced predictive maintenance techniques can certainly reduce effort and time costs because maintenance is often easier than outright repair. Also, in line with the earlier figure of $260,000 per hour of downtime, predictive maintenance results in a reduction in the amount of time that machines are kept offline for repair.
2. Increased Productivity
With lower downtime naturally comes improved productivity. But on top of that, having a workflow that integrates maintenance schedules based on the results of predictive analytics can help your business create adaptive and backup processes to keep production lines going while other machines are out for repair. Overall, machine uptime can be increased by as much as 9%, based on the study.
3. Longer Machine Lifespan
The PwC study illustrated that IoT-based predictive maintenance can improve the lifespan of an older machine by as much as 20%. By preventing destructive, catastrophic failure and ensuring that all repairable and maintainable faults are detected, the usable lifespan of a machine can be extended.
4. Improved Safety
When a machine fails, there is always the risk of a workplace safety hazard. Having IoT-based predictive maintenance reduces the risk of encountering a machine failure event that might harm factory workers.
Over time, IoT sensors can also identify potential hazards and alert workers to reduce exposure to those areas. Indeed, respondents in the PwC study demonstrated a reduction in safety, environmental, health, and quality risks by as much as 14%.
IIoT and Predictive Maintenance: A Match Made in Productivity Heaven
The remote sensing, diagnostic, and analytics capabilities of IIoT have always seemed like a perfect fit for predictive maintenance. Now, with modern Industry 4.0 implementations, the benefits of PdM can truly be unleashed more efficiently than ever before.
Of course, running predictive maintenance involves the generation and processing of huge amounts of data from an extremely large number of connected resources. Managing such data at scale requires expert integration of cloud resources with these devices, as well as the business systems and workflows that can be automated from this data.
At Reekoh, we offer tailored industry solutions that can integrate data from your assets into a system for predictive analytics and maintenance. Our methods can help you efficiently gather, store, and process large amounts of data and help you utilize it to reduce your downtime, improve your workflow, and reap other benefits of PdM.