The Maintenance Methods That Are A Cut Above the Rest

use the IoT for predictive and condition-based maintenance

Perform Maintenance at the Right Time For Efficient and Cost-Effective Asset Performance

If you're a factory or facility maintenance manager, proactivity is the name of your game to keep things running efficiently and avoid downtime. Rather than work reactively to keep equipment, machinery, and people productive, there are popular proactive maintenance programs that have proven much more rewarding. As technology like the Internet of Things (IoT) advances, predictive and condition-based maintenance (CBM) methods only get better.

These are the maintenance programs where IoT sensor data shines. Real-time and trending data from connected machines help maintenance leaders and technicians work smarter, more efficiently, and proactively. Even though there are benefits, you're instigating the least efficient and possibly most costly maintenance strategy by operating in reactive maintenance mode. So, when you employ a predictive or CBM program, you work at the highest productivity and cost-effective levels.

What is Predictive Maintenance?

To estimate when maintenance is required, predictive maintenance methods help assess the current condition or degradation of equipment or machinery. This potentially saves precious and sometimes scarce resources of effort, time, and money versus the possibly costly, time-restrictive preventive maintenance approach because you only perform maintenance tasks when needed.

This means condition-based maintenance using condition monitoring with sensors is a predictive maintenance method. You can also conveniently schedule corrective and preventive maintenance activities as part of a predictive maintenance program to prevent unexpected failures.

Predictive maintenance uses IoT sensors to monitor critical parameters on or in equipment, machines, and systems. This sensor data allows you to analyze real-time and historical system health trends to predict a breakdown. You then perform maintenance more efficiently and cost-effectively.

IoT for Predictive Maintenance

What is Condition-Based Maintenance?

You perform condition-based maintenance (CBM) only when it's necessary. The method has been around for a while and is part of the newer predictive maintenance method with AI, IoT wireless sensors, and connectivity. That's why the CBM acronym is also used when referring to IoT condition-based monitoring. With CB monitoring and maintenance, you maintain equipment, machinery, or things when data shows they will fail or break down soon.

A CBM program or system:

  • Uses real-time data to prioritize and optimize maintenance resources.
  • Supports methods that integrate with active redundancy and fault reporting and those that don't.
  • Determines a machine's health and only goes into effect when required.
  • Integrates internal instrumentation and systems with data from sensor technology to help you decide the best time to perform maintenance.
  • Helps maintenance personnel service only the right machinery or equipment, reducing spare parts cost and overall downtime.

Maintain the Right Things at the Right Time

The CBM method, combined with its parent predictive maintenance strategy, answers the question: When is the right time to perform maintenance?

Integrating the IoT and CBM in predictive maintenance also:

  • Modernizes how you manage critical assets.
  • Connects IoT wireless sensors and the cloud to machines, systems, and equipment.
  • Lets you collect real-time data about critical performance indicators like temperature, vibration, and energy consumption.
  • Incorporates data from smart facilities, including building controls, software, metering devices, and technician reports.
  • Enables learning from alerts and failures to optimize maintenance practices, update operating procedures, and identify trends and indicators.

In addition to instant alerts or data points when a threshold is breached, you can turn data analysis up to today's systems using AI, machine learning algorithms, and predictive models. Although, you don't have to access these advanced analytics systems to predict maintenance. Our iMonnit Software connects to your sensor networks to continually monitor and identify anomalies and display dashboards about asset performance to help you predict potential failures.

Then, you can plan and schedule maintenance based on the condition of essential things instead of fixed intervals, optimizing resources and reducing downtime. The IoT enables remote diagnostics, helping maintenance teams access and analyze asset data from nearly anywhere, perform troubleshooting, and complete only required repairs.

Stay on the Path to Transformative Benefits

When your combined predictive and CBM program is in full swing, you reap the benefits or ROI of these proactive maintenance strategies. In addition to the benefits mentioned earlier, you can improve efficiency, lengthen critical asset life cycles, save on operational and capital expenses (OPEX and CAPEX), and boost overall asset performance and reliability.

With a solid predictive maintenance method, you can easily transition into what our founder and CEO Brad Walters calls the transformative method within his Law of the IoT. He says that when you achieve the value of a predictive method, you can begin to transform processes and entire business operations using IoT sensor data in three ways:

  1. Get a complete view of your asset life cycle.
  2. Combine different datasets into one analytics solution.
  3. Create additional cost savings and better ways of doing business.

After you put a predictive maintenance program to work, this is how you'll experience the transformative and exponential value of the IoT in your organization.

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