- Predictive maintenance takes preventive maintenance to the next level, enabling businesses to attend to equipment well before it needs repairs.
- Predictive maintenance costs less than reactive maintenance and saves on costs that result from repeated repair and downtime.
- Tips to establish predictive maintenance include starting small, identifying predictive maintenance-ready tasks, and determining the resources you’ll need to get started.
- This article is for businesses that want to implement a more targeted equipment maintenance program that can predict problems and save money.
Leaders at any business that depends on complex machinery or devices know that regular maintenance is essential to smooth and efficient operations. Without timely maintenance, machinery breaks down, leading to downtime, costly repairs and sometimes even replacement. The common practice of preventive maintenance entails regularly inspecting equipment and tuning it up, before it needs repairs. But the emerging practice of predictive maintenance aims to build upon preventive approaches and make them more efficient and cost-effective.
What is predictive maintenance?
Predictive maintenance is the practice of monitoring equipment via sensors, software and data feedback to prioritize equipment for proactive maintenance. It takes preventive maintenance one step further by streamlining the process of identifying which equipment (or even which components) is showing signs of needing attention. Predictive maintenance can save businesses money by identifying exactly when they should tune up equipment, rather than just maintaining a regular preventive schedule that could be directing resources to equipment that doesn’t need maintenance.
How predictive maintenance works
Predictive maintenance relies on internet of things (IoT) sensors and devices, which are wirelessly connected to a console that collects and analyzes data from the machine. Sensors detect a variety of data, feeding it to a computer that presents it to you in a manageable form. The data provided from IoT sensors, such as temperature and vibrations picked up with ultrasonic detection, can tell you a great deal about how a machine is running.
Vibration sensors detect any subtle, unusual changes. Thermic sensors determine if there is too much friction on certain moving parts. Other sensors monitor oil and lubricant levels to ensure that there’s enough and that it is clean.
Each sensor feeds data back to a centralized source, where machine learning algorithms break down the data and put it into the context of machine performance and wear. Utilizing the mountains of collected data, IoT programs alert you to when maintenance is needed or if a breakdown is imminent, thereby allowing you to dispatch maintenance teams in a targeted way.
Key takeaway: Predictive maintenance combines the internet of things with predictive data analytics to improve the way businesses set maintenance schedules. As a result, businesses can allocate resources more efficiently, boost the longevity of machines and maximize uptime.
Benefits of predictive maintenance
Predictive maintenance offers improvements over the current widespread standard of preventive maintenance. This results in several clear benefits:
- Reduced maintenance costs: Predictive maintenance reduces maintenance costs by allocating resources and labor only to equipment when it needs attention. Whereas preventive maintenance typically relies on a set schedule, predictive maintenance analyzes when a machine or device actually needs attention.
- Fewer major equipment failures: Predictive maintenance quickly identifies a problem with equipment, thus enabling maintenance crews to address the issue before it results in a catastrophic failure that hurts productivity.
- Longer equipment life span: Like preventive maintenance, predictive maintenance is intended to extend the life span of equipment. By keeping tabs on how equipment works both as a whole and on the component level, replacement parts can be ordered as needed and maintenance crews can keep machinery in top form, thus increasing its longevity.
- Auditable documentation trail: Because predictive maintenance involves the collection of vast amounts of data, it provides an auditable paper trail that can be used to back up warranty claims or to meet Good Manufacturing Practice (GMP) or ISO standards.
Tips for implementing a predictive maintenance routine
When you’re implementing a predictive maintenance system, be sure to consider the scale of the operation. Sophisticated systems for different machines can be expensive, and while costs are starting to decrease as the technology becomes more readily available, businesses should consider many factors when establishing a predictive maintenance process.
First, ensure someone on staff understands the system and knows when maintenance should be performed. This role requires a mix of expertise in IT and knowledge of the machines and equipment being monitored. Depending on the scale of your operation, it may be necessary to hire an IoT expert.
Consider the following tips to set yourself up for success with predictive maintenance:
- Start small. Given that this system takes some effort to set up, it’s important to start with just one or two assets before implementing the process across the company.
- Identify predictive maintenance-ready tasks. Determine which assets are best prepared to handle such a process. For instance, resources that are expendable or that require minimal maintenance may not be a good fit for predictive maintenance.
- Determine the required resources. Figure out what you need in order to implement predictive maintenance. Here, the key categories are labor, materials, facilities, technology and employee training.
- Implement asset monitoring, and begin collecting data. Start monitoring your chosen assets. The data collected during this time becomes the foundation for your entire predictive maintenance system. Three of the most common methods of data collection are electromechanical systems, thermography, and lubrication and wear.
- Create machine-learning algorithms. Using the data collected in the previous step, develop your own algorithm that will help you predict equipment failure before it actually occurs.
- Apply the algorithms to the pilot asset. Use sensors to collect the data, apply predictive maintenance algorithms to your data center, and generate reports and insights based on this information.
- Continually improve the process. Use your results to make the process more efficient and effective moving forward.
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Predictive maintenance is smart business
Predictive maintenance can require a substantial investment, but it can also save your company money in the long run by targeting problem areas that preventive maintenance can’t address on its own. Once you understand the costs and benefits, you’ll know whether, and where, predictive maintenance can help your business prevent costly downtime and repairs.
Adam C. Uzialko also contributed to this article.