In Part 1 of the Predictive Maintenance (PdM) series, we spoke of the various benefits of PdM and explained how advanced IoT-based tech has been integral in effectively obtaining these benefits. Since most maintenance functions today follow a reactive maintenance philosophy, making the leap to PdM, or even a hybrid maintenance model, might seem like a daunting task. This article explains the steps involved in integrating PdM-sensing infrastructure in your manufacturing setup in order to facilitate a world-class maintenance practice.
First, you will need the following elements in place to facilitate IoT-based predictive maintenance:
1. Installing advanced sensors at specific points helps collect equipment data, such as battery health, electricity voltage, operational temperatures, and machine vibrations, in real time.
2. A well-developed data conveyance system is required for data to be transferred from the aforementioned sensors to a centralized data repository. This will typically include on-site field gateways, through which data is filtered and pre-processed, then sent to the cloud for added security and connectivity.
3. Predictive analytics software are iteratively developed ML-based programs that are applied to the data to generate actionable insights regarding equipment performance and condition. This software sends notifications to maintenance teams when machines operate beyond user-defined ranges. It also generates and stores operations reports for future analysis.
When transitioning to a hybrid or fully predictive model of maintenance, framing the problem, scoping the availability of data, and assessing predictions are the three primary factors that will determine success. If your next natural question is about how to begin implementing IoT-based PdM, not just on an as-needed basis but continually and in real time, the following are the first steps to take:
To begin with, instead of starting with an overarching application, you should begin with analyzing all assets to determine which ones will produce the highest ROI in the short run if included in a PdM model. Concentrating on the business KPI element of this framework is important to prevent significant losses from occurring during the initial stages of implementation, which is quite possible given the overhead costs.
It is natural that historical data about previous usage, performance, and maintenance is required for analysis since the aim is to forecast future failures. Such data should typically go back years in order to fully comprehend the lifecycle of the machinery. Past equipment failures that have occurred should be listed and ranked based on frequency and impact to conduct a pre-implementation analysis. Additionally, general static data of the system, such as its mechanical features, typical usage, and operating circumstances, can offer you useful information to design a predictive maintenance model. The presence of existing sensors on all equipment as well as the predictability of each asset’s failures are two additional factors that must be taken into account here as well.
IoT-enabled PdM has the potential to transform maintenance completely and forever, providing benefits that range from added cost savings to improved worker safety. However, a well-developed architecture with emphasis on ML techniques is required for a robust predictive maintenance system.
The implementation plan for predictive maintenance described above can be summed up as follows:
Predictive maintenance is expected to be in widespread adoption as a result of falling hardware and software prices. In the near future, investing in these technologies, working with analytics professionals, choosing the right software, and identifying assets that need maintenance will provide companies that adopt this model a significant competitive advantage in inevitable digital transformation of the future.