Predictive maintenance with Amazon Lookout for Equipment
Cost savings to maximize profits have always been one of the primary business objectives, as much is gained by not losing. In the data era, we have tools that bring these processes to maximum effectiveness, such as predictive maintenance, which allows us to optimize equipment care processes to achieve peak performance from them.
In this publication, we will focus on the specific service offered by the Amazon Cloud for this purpose, Amazon Lookout for Equipment, which we use in some of our projects as Amazon Web Services (AWS) partners. The goal is the same as all predictive maintenance systems: to minimize breakdowns and downtime, reduce spending on spare parts, improve decision-making, and ultimately, achieve the highest levels of productivity from equipment. But how does it do it?
To monitor machinery, the first thing we need is to capture the data it generates. In this regard, Amazon Lookout for Equipment allows for handling inputs from up to 300 sensors and uploading activity logs with which it will perform its forecasts. Once the raw material is obtained (pressure, flow rate, revolutions per minute, vibration, temperature, power…), it analyzes it and applies machine learning to transform that data into useful information to act upon based on the true needs of the machinery, rather than on pre-set schedules unrelated to the reality of the equipment.
The thousands of data points provided by the sensors will be automatically screened based on a pre-established model that follows the specific characteristics of the company. Using the determined standard records as a basis, the system will display anomalous machinery behavior. Upon detecting an alteration in any type of parameter, Amazon’s predictive maintenance tool determines which sensors are hosting atypical measurements and warns of the scope of the recorded anomaly, enabling rapid responses to the problem.
Machine learning
Furthermore, by basing its system on machine learning, Amazon Lookout for Equipment improves its own performance by learning from the information provided by operators and their actions regarding detected deviations. In this way, once a parameter that differs from the authorized standard is revealed and an alert is issued, the system evaluates the human response (whether it resulted in a shutdown, was a false alarm, the machine was allowed to continue operating…) to make the issued warnings increasingly accurate. This tool can learn, for example, that an increase of X degrees is not important if it is not accompanied by a simultaneous increase in pressure, so it will wait to issue the alert until these factors converge and it is truly important to inform the workers.
Having precise, accurate, and easy-to-interpret information in real time allows for rapid forecasts, enabling early action at the first signs of failure or low performance of a machine. Thus, as we have seen, it is possible to improve planning and prevent unscheduled downtime and major breakdowns, ultimately reducing inactivity time and increasing machine performance. Consequently, there are also savings in labor time and spare parts, as components will only be replaced when truly necessary and not preventively, which would shorten their useful life.