Predictive maintenance is changing the way that machines are being looked after. This internet of things (IoT)-enabled technology is enabling machines to indicate when they might break down, prior to the event. By reducing machine downtime, you can save money, meet demand, and keep your customers happy.
Predictive maintenance is the process of predicting a machine’s performance, status, and real-time health by using condition-monitoring sensors. With predictive maintenance, machines are equipped with sensors that are connected to IoT-enabled software that gives users updates and notifications. The IoT-enabled software will absorb vast amounts of data. This data can then be run through various algorithms that can be used to accurately predict when a future breakdown or outage may occur. This form of machine learning allows predictive maintenance to prevent running unnecessary maintenance checks, further reducing waste in terms of time and effort.
Predictive maintenance is not to be confused with preventative maintenance. Preventative maintenance is the process of scheduling maintenance checks in accordance with usage limits or a present schedule. Although this method reduces machine downtime, it is still not an absolute way of knowing whether the machine is likely to break down or not.
Predictive maintenance is ultimately about increasing the interval between any maintenance procedures. This has many benefits for your business, including:
The initial investment in IoT-enabled predictive maintenance will be justified by your return on investment. But how and where is the best place to start?
When implementing large-scale organizational change, you need to start small to ensure adoption among employees. Focus on rolling out a successful predictive maintenance pilot program first.
Some components or pieces of machinery do not require predictive maintenance. As an alternative, lower cost processes may be more cost effective. Similarly, you shouldn’t start with a major, critical piece of machinery. By the time you have learned from your pilot program, you should have better processes in place to run the program.
Before implementing a predictive maintenance machine learning system in your workplace, you should determine which resources you will require. Calculate your required usage of:
Now that you have your technology and manpower, and have identified your pilot asset, you can start to collect some preliminary data. The most common ways of collecting your data will be:
Now that your production line is starting to collect data on itself, you can start to analyze the data. Formulas, algorithms, and machine learning tools can be used to translate this data into actionable maintenance timelines. The benefit of using machine learning predictive maintenance is that the data collected can be used to be better informed for future product design.
In line with a manufacturing strategy, you must be constantly improving your machine learning predictive maintenance process. Make it part of your process to continually determine if there might be more useful data to collect, or if your algorithms could be giving more accurate results. The benefit of machine learning is that after you’ve collected your first data set, the predictive maintenance model will continue to improve over time.
Predictive maintenance needs to be at the heart of any modern manufacturing business. Lowering inefficiency and maximizing value will give any manufacturer the competitive edge they need in today’s industrial landscape. Predictive maintenance machine learning delivers just that. Find out how you can start your digital transformation with PTC.