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Can Predictive Maintenance Protect Your Business?

Business News Daily Editor
Business News Daily Editor

Learn the benefits of IoT-powered maintenance and determine if it's right for you.

  • Predictive maintenance is a useful practice that allows businesses to maintain machines before they break.
  • Predictive maintenance can be beneficial to companies because it provides a great return on investment, it's less expensive than reactive maintenance and can help save on repetitive costs.
  • Some tips for establishing predictive maintenance in your company are to start small, identify PdM-ready tasks and identify the required resources.

Any business that depends on complex machinery or devices knows that regular maintenance is essential to keep them functioning and running efficiently. Those in manufacturing know that without timely maintenance, machinery breaks down, leading to downtime and costly repairs, sometimes even requiring a replacement. The common practice of preventative maintenance entails regularly inspecting equipment and tuning them up, whether they need it or not. An emerging practice, however, aims to make maintenance much more efficient and cost-effective.

What is predictive maintenance?

Predictive maintenance is the practice of maintaining machines before they break down. Certain machines may run better than others so businesses could save on downtime and maintenance costs if they know exactly when they should tune equipment up rather than keeping a regular tune-up schedule.

How predictive maintenance works

Predictive maintenance relies on in most part internet of things (IoT) sensors 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. From temperature to vibrations and ultrasonic detection, the data provided from IoT sensors 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 there's enough and that it is clean.

Machine learning breaks down the data and puts it in the context of machine performance and wear. Utilizing the mountains of data collected, IoT programs alert you to when maintenance is needed or if a breakdown is imminent.

How will it help my business?

Predictive maintenance has several benefits for businesses, the first being that it provides a great return on investment and has the potential to save your business a lot of money. While preventative maintenance is a good practice to follow and will certainly be less expensive than reactive maintenance (when a machine breaks down), it can still lead to unnecessary costs and downtime. Preventative maintenance is used, because we don't know when a machine will need essential care, a tune-up or replacement parts. With predictive maintenance, we can foresee when a machine needs care, saving on repetitive costs.

Comly Wilson, director of digital marketing with commercial real estate maintenance company Enertiv, said the benefits of predictive maintenance are clear, thanks to collected data. IoT predictive maintenance used by the company reduced maintenance costs an estimated 25%, with a 50% reduction in major equipment failures and extending equipment life 20 to 36%.

Another benefit of IoT predictive maintenance is the ability to generate an auditable trail of machine performance and behavior that you can use to your advantage should you have to file a warranty claim, said Brian Gratch, CMO with IoT company Xaptum.

Making predictive maintenance work for you depends on how widescale you want to implement the process. Sophisticated systems for different machines can be costly, and while costs are decreasing as the technology become more readily available, businesses wanting to implement a system should do so sensibly.

Computerized maintenance management system (CMMS) maker Fiix offers several tips for implementing a predictive maintenance system, including ensuring that you deploy IoT on machines and equipment that serve a critical role in operations and would be highly disruptive to the business if they failed. Don't waste money implementing predictive maintenance on equipment that is not as crucial to operations. It's also important to use it on equipment where IoT sensors can easily detect problems. Not machinery and equipment are good candidates for IoT sensors. Making sure to include predictive maintenance where it really matters will save on costs.

Another cost to consider when implementing predictive maintenance is to have someone on staff who is qualified to understand the system and knows when maintenance should be performed. This role requires a mix of expertise in IT and a knowledge of the machines and equipment being monitored. Depending on the scale of your operation, it may be necessary to hire an IoT expert.

Tips on establishing a predictive maintenance program

If you are seeking some tips on establishing a predictive maintenance program within your company, according to Upkeep Blog, some great tips are as follows:

  • Start small. Given that this system takes some effort before it becomes efficiently implemented, it is important to start small with just one or two assets before implementing the process companywide.

  • Identify PdM-ready tasks. Next, you need to figure out which assets are best prepared to handle such a process. For instance, resources that are expendable or only require minimal maintenance may not be a good fit for predictive maintenance.

  • Identify the required resources. The next step is to identify the required resources to implement predictive maintenance. The key categories of which are labor, materials, facilities, technology and training.

  • Implement asset monitoring and begin collecting data. Begin monitoring your chosen assets. The data collected during this time becomes the foundation for your entire predictive maintenance system. Three of the most commonly used methods of collecting data 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 to the pilot asset. Apply the algorithm to the pilot asset by using sensors to collect the data, applying PdM algorithms to your data center, and generating reports and insights based on this information.

  • Establish continuous implementation and improvement process. Last, take your results to drive continuous improvement of the process moving forward.
Image Credit: dusanpetkovic / Getty Images
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