Before the advent of Industry 4.0, regularly scheduled preventive maintenance was the primary method used to mitigate downtime and ensure reasonably smooth equipment operation. Since preventive maintenance is based on regular cycles, however, it has been an inexact and costly practice. For preventative maintenance, workers must be continually trained to understand machine breakdowns and maintain their skills. Determining the proper maintenance schedule can be difficult, sometimes leading to over-maintenance of machines that could, ironically, increase the amount of unnecessary downtime. According to an ITIC survey, 98% of enterprises report losing at least $100,000 per hour of downtime, with 33% of those surveyed claiming to lose from $1 million to $5 million per hour. Therefore, any maintenance performed on machines must be geared toward downtime reduction.
As the technology becomes more commonplace, facilities have utilized IoT to implement efficient predictive maintenance practices. Machine-integrated sensors tasked with collecting data sets and sending them through secure pathways to cloud-based management platforms provide a more holistic picture of machine health. Operators can apply the gathered usage information to predict and implement effective servicing cycles, greatly reducing overall machine downtime. The impact of predictive maintenance already has proved significant in streamlining operations and reducing costly downtime states. According to a report from McKinsey & Company, IoT-enabled predictive maintenance could potentially save manufacturers $200 billion to $600 billion by 2025.
However, predictive maintenance is not the end of the road for increasing efficiency through IoT. Developers have continued to push predictive maintenance analytics further with constant analysis of equipment, allowing operators to accurately project when the next service will be required and signaling alerts accordingly. These IoT predictive maintenance tactics fall under the umbrella of condition monitoring (CM), which is the process of observing machinery condition parameters in order to identify changes that are indicative of a developing fault. Effective CM addresses conditions that would shorten normal machine lifespan before they develop into a major failure. As a result, CM is a major tool in reducing downtime and ensuring effective maintenance through IoT.
How Factories Use Condition Monitoring to Maintain Equipment
Since the problems that cause machine downtime take many forms, the types of IoT-enabled condition monitoring vary widely between machines and should be customized from application to application. Industrial machinery equipment failure causes 42% of annual unplanned downtime, according to a report from the Wall Street Journal, and effective IoT-enabled condition monitoring can do much to reduce machine breakdown. IoT sensors either incorporated into machine design or retrofitted on legacy equipment can provide operators with a wide range of performance metrics, including pressure, vibration, temperature, voltage, and battery charge. CM technologies, such as lubricant analysis, ultrasound material thickness testing, motor current signature analysis (MCSA), and infrared thermography, can transmit data to a management platform that can analyze the data and predict damage that will lead to failure. With data in hand, factory operators then can schedule preventative maintenance before potential damages become critical.
How Condition Monitoring Prevents Downtime in Automotive Fleets
Automotive downtime is a universal challenge faced by companies that utilize fleets. According to Automotive Fleet, every hour of automobile downtime results in an average loss of $79.32 per hour, per driver, without factoring in the expenses incurred by rolling trucks and making repairs. To reduce this downtime and more effectively utilize maintenance resources, fleet managers have turned to CM IoT systems to monitor vehicle health. Factors like engine temperature, vehicle vibration, and fuel consumption can be measured to detect potential faults before they happen. When any condition moves beyond its standard operating threshold, the sensors can trigger alerts and divert the vehicle to a service center. CM IoT systems also can build a profile of each fleet driver to monitor their impact on the vehicles condition, which is useful for managing driver risk and pinpointing any operator weakness.
Improve Your Maintenance Cycles with Aeris
Aeris IoT Services stands at the forefront of IoT connectivity, helping our clients achieve their predictive maintenance goals with robust technology that is managed from the user-friendly Aeris Mobility Platform. As condition monitoring technology becomes an integral part of Industry 4.0, Aeris clients have ample IoT resources at their disposal to streamline maintenance practices and predict failures before they happen. Whatever the application, the process of preventive maintenance is greatly improved through IoT-enabled predictive analytics and condition monitoring powered by Aeris.
To learn more about how your company can streamline its maintenance practices, contact Aeris today.