The importance of predictive maintenance
Work stoppages cost companies money. It’s neither a secret nor a great mystery. The longer the stoppage and the bigger the company, the greater the revenue loss and operating cost respectively. Fortunately, this is a reality that most companies do not have to worry about. However, for the manufacturing and energy sectors, this is not a rare occurrence. This is where predictive maintenance comes into the picture.
Modern manufacturing facilities are growing more complex with increased data connections, integrated hardware, and automated systems. To address maintenance and equipment challenges, organizations have turned to AI and IoT technologies. This has helped organizations analyze and predict performance and improve the quality of data collection and analysis.
Why predictive maintenance?
Manufacturing and oil & gas industries are majorly dependent on heavy machinery that can be scattered across large distances, and often in remote locations. Some examples include pumps, rigs, turbines, boilers, conveyors, etc. So, to maintain these machines, engineers need to make regular scheduled trips and stay on-site all the time. That’s quite the investment in manhours and headcount!
Even then, the machines will break down over time. There are just too many internal and external influences for engineers to keep an eye on. In this digital age, the energy industry is largely still running on manual power. When there are solutions powered by artificial intelligence available to be incorporated into the process to make the business more efficient, they should be considered. Our advances in technology have led to a digital transformation wave where AI takes on menial tasks and provides advanced analytics, leaving humans to operate at a higher and strategic level.
How does predictive maintenance help?
Predictive maintenance is an AI and machine-learning-powered solution that will do just that for manufacturing and the oil and gas industry. It takes historical data to predict which parts of a machine will fail at what time. With proper sensors installed that provide accurate and relevant data points, the prediction engine will operate with precision and prevent work stoppages.
Using the predictive maintenance solution, businesses will know when to schedule specific parts to be replaced, be alerted to degradations due to faulty parts or installations, if any parts are being cluttered and need to be cleaned, etc. The solution can even be adapted to prevent oil spills by predicting tank overflows and when to schedule replacements!
How does predictive maintenance work?
The initial step in the predictive maintenance process involves collection of real-time data from the connected IoT network assets in the overall process. This includes collecting data from physical sensors like thermal imaging, ultrasonic, motion & flow sensing, vibration, etc.
Secondly, this data needs to be transmitted from sensors to the central business system. Here, the data is stored and managed to make it ready for processing and analysis. Later, intelligent technologies like AI and machine learning analytics are applied to processed data to derive useful and actionable insights.
Finally, the management can take rapid actions based on the data-driven insights.
Unfortunately, it is one thing to install predictive maintenance and another entirely to make full use of it. Just like how it wouldn’t be wise to purchase a Tesla if one doesn’t have a driver’s license, charging port, and lives in northern Canada without a garage, businesses have to be ready to adopt predictive maintenance. How well equipped and ready a business is for adoption depends on many factors. But the journey to implementing a fully functional predictive maintenance solution is well worth the time, effort, and money if it prevents the annual machine breakdowns and work stoppages, don’t you think? To get started with implementing predictive maintenance, contact us!
- Why predictive maintenance is incomplete without explainable AI
- Predictive maintenance for manufacturing equipment
- How to know your business is ready for predictive maintenance
This blog was originally published 3/18/2020 and has since been updated.