The Internet of things (IoT) is transforming the way manufacturers operate. One of the greatest advantages of IoT technology is its use in predictive maintenance, which focuses on preemptive planning for equipment breakdowns, helping businesses make the most out of their resources.
In order to gain this level of, IoT-based predictive maintenance systems make use of data-collecting sensors to gain insight from machines and equipment about operating conditions, as well as software analytics to generate reports about potential problems or failure risks. This reliable data gives manufacturers greater control over when service needs are required and what components may need replacing so that proper allocation of resources can be planned out in advance. This, in turn, can save manufacturers on expensive repair costs while forecasting future requirements more easily.
IoT-based predictive maintenance is crucial for ensuring machine reliability and safety. Machine data is collected, which can include operating temperature, supply voltage, current, and vibration, through sensors and wireless transmission. The collected data is sent in real time to a cloud-based centralized data storage platform. Maintenance teams gather data from the centralized storage system and analyze it using predictive analytics programs, powered by AI, and machine-learning (ML) algorithms to derive actionable insights to guide repair or preventative maintenance.
Security is another important factor to consider when implementing an IoT solution for predictive maintenance. IoT technologies gather personal information from various sources that must be stored securely to prevent malicious activity like cyberattacks or data breaches. Data privacy regulations can differ depending on the country or region, and compliance is necessary. ML technology and IoT solutions together can transform preventative maintenance.
Implementing IoT-based predictive maintenance has multiple benefits to manufacturers. Reducing maintenance costs is a primary concern, with the ability to schedule optimal inspection and maintenance routines that can avoid unplanned downtime to remain cost-efficient. Enhanced asset reliability is another benefit that can result from accurate forecasting and avoidance of machine failures, leading to higher rates of machine utilization and increased profitability.
Using sensors on machines gives continuous feedback regarding data such as temperature, vibration levels and operating conditions. The data gathered by sensors and connected analytics tools can be converted into actionable insights that reveal potential maintenance issues before they cause equipment failure or a costly repair job. With IoT-based predictive maintenance identifying potential errors before they occur, operational efficiency can be maximized as unexpected downtime and other associated risks are minimized.
The components that comprise IoT-based predictive maintenance are sensors, data communication, central data storage, and predictive analytics.
Sensors have become an integral part of modern technology, enabling the real-time collection of data from various devices, systems, assets, and locations. This data is critical for businesses to optimize their operations, improve efficiency, and make informed decisions. With sensors, the performance of machines, inventory levels, energy consumption, and much more can be monitored, tracked, or measured. By collecting data in real time, businesses can quickly identify issues and take corrective action to reduce downtime and improve productivity.
Data communication is the process of transmitting or transferring data from one device to another. It plays a crucial role in sending data collected from various devices to a central data storage system in the cloud for the efficient management and storage of data, as well as easy access to data from anywhere at any time. The data communication process can involve various protocols and technologies such as TCP/IP, Wi-Fi, Bluetooth, and Ethernet.
Central data storage in the cloud is becoming increasingly popular. Data can be stored and accessed from anywhere at any time with an internet connection, meaning that businesses can centralize their data storage for easier management of and access to important information. Cloud storage is often more secure than traditional methods of data storage, with advanced encryption and backup systems in place to protect against data loss or theft. Central data storage in the cloud offers many benefits and is quickly becoming the preferred method of data storage for both businesses and individuals.
Predictive analytics is a powerful tool that enables maintenance teams and repair engineers to stay ahead of equipment failures and breakdowns. By using real-time data streams, predictive analytics can identify potential issues before they become major problems, saving not only time and money, but also helping to improve the overall efficiency of the maintenance process. In addition to real-time data, periodic reports are also available for further analysis and to provide valuable insights into equipment performance to fine-tune maintenance schedules and repair procedures. Predictive analytics is a critical component of modern maintenance and repair operations.
Manufacturing industries are among the largest adopters of IoT predictive maintenance. The manufacturing industry uses this technology to monitor equipment, detect anomalies, and identify potential failures to help manufacturers to schedule maintenance and repairs before machinery breaks down, reducing unplanned downtime and increasing production capacity.
IoT-based predictive maintenance helps the service industry to monitor equipment performance, predict potential failures, and schedule maintenance and repairs, reducing time spent on reactive maintenance. It also helps service-related companies to provide better customer service by reducing downtime and improving equipment reliability.
Life sciences companies have also adopted IoT-based predictive maintenance to ensure equipment reliability, monitor laboratory equipment such as refrigerators, freezers, and incubators, and reduce downtime. IoT-based predictive maintenance in the life sciences industry helps to ensure that equipment is maintained at the correct temperature and humidity to reduce the risk of equipment failure and protect valuable samples.
IoT-based predictive maintenance uses sensors, analytics, and ML algorithms to predict when a machine or piece of equipment will require maintenance, enabling companies to reduce not only maintenance costs, but also downtime. This also helps companies to identify potential problems and address them before they become major issues. IoT-based predictive maintenance is a powerful tool that can help companies keep costs down and improve overall efficiency.
Businesses can increase asset utilization with IoT-based predictive maintenance by predicting and preventing equipment failures before they occur. Using sensors and other IoT devices for data collection about equipment performance can provide businesses with valuable insights into potential issues and proactive steps to address them. This not only helps to avoid costly downtime and repairs, but it also allows businesses to maximize the lifespan of their equipment, as well as overall productivity. IoT-based predictive maintenance lets businesses stay ahead of the curve with the confidence that their assets are always working at peak efficiency.
IoT-based predictive maintenance can also improve technician efficiency by providing real-time information about equipment performance that can help technicians identify potential issues before they become major problems, enabling them to schedule maintenance at a time that is convenient and cost-effective. This approach can help businesses reduce the time and resources required for maintenance, freeing up technicians to focus on other tasks.
Detecting issues early and scheduling maintenance at a time that’s both convenient and cost-effective can reduce equipment downtime. IoT-based predictive maintenance is rapidly gaining popularity in the manufacturing, energy, and transportation industries to reduce equipment downtime and optimize operations. By utilizing sensors, data analytics, and ML algorithms, businesses can predict equipment failures before they occur and schedule maintenance proactively to not only reduce the cost of unscheduled downtime, but also improve the overall efficiency and lifespan of equipment.
Improve safety and compliance—IoT-based predictive maintenance can also be a powerful tool for the improvement of safety and compliance in a variety of industries by monitoring equipment in real time and ensuring that equipment is always in good working order. With potential safety hazards identified, steps can be taken to address them before they present a safety hazard or lead to regulatory noncompliance of equipment standards.
Not all equipment requires predictive maintenance. Determine which machines would benefit the most from minimal downtime while also considering the impact on your bottom line. Rank assets based on past downtime incidents and resulting business loss, starting with those most critical, to implement IoT-based predictive maintenance.
With so many options available, finding the right tools for predictive maintenance can be overwhelming. A computerized maintenance management system (CMMS) might make it easier to find a tool that suits a particular need. Read detailed user reviews to get a better idea of tools and to learn what's good about them and what's not.
Machine learning and algorithms can assess an asset's condition and predict when a failure will occur. Continuously monitor and report on the asset's performance to determine if a strategy is working. If it is, consider expanding it to other assets to enhance productivity and reduce unplanned downtime. IoT-based predictive maintenance can improve productivity and maximize the performance of an asset throughout its lifecycle.
Anthony Moffa is a Senior Director within PTC’s ThingWorx Product Management team. He has extensive experience, designing, manufacturing and implementing diagnostic systems in a variety of industries including aerospace, nuclear power and petrochemical. Prior to joining PTC he was responsible for the design and implementation of 2 IoT programs, one in life safety and the other in the life sciences arenas. He has been a long-time contributor to service research advisory councils managed by Aberdeen and The Service Council, holds a Mechanical Engineering Degree from Villanova University and has multiple Six Sigma certifications.