White Paper: Predictive Maintenance in Manufacturing — Everything You Need to Know

Predictive maintenance in manufacturing helps to reduce downtimes and cut maintenance costs. Based on the data collected from sensors, a predictive maintenance system monitors and analyzes equipment and identifies patterns that can potentially cause equipment failures.

Taking historical data and working based on a probabilistic approach, ML-based predictive maintenance solutions define their own set of rules, according to which PdM systems generate outcomes about equipment state and send notifications to the personnel.

Unlike preventive maintenance, predictive maintenance doesn’t intend to take maintenance activities on a regular basis. It monitors the equipment state and suggests any maintenance steps only if the equipment or its parts are going to degrade. This way, manufacturers can avoid unnecessary maintenance and costly downtimes.

The first step is to install sensors on your equipment to collect real-time data on performance metrics such as temperature, vibration, and pressure. The next step is to use machine learning algorithms to analyze this data and identify patterns that could indicate potential failures. Refer to our predictive maintenance white paper to get the key steps and best practices for successful predictive maintenance implementation.

The need for accurate data collection, the integration of advanced technologies with existing systems, and the expertise required to analyze the data are among the main challenges of adopting predictive maintenance. Still, following predictive maintenance best practices, such as ensuring data quality or continuously refining the models, can help companies to mitigate these challenges and lead to successful implementation.

One example includes using sensor data to monitor the health of critical equipment, such as motors or pumps, to predict when they are likely to fail. In our predictive maintenance white paper, we describe several success stories how Intelliarts built predictive maintenance solutions to help manufacturers reduce downtime.

The future of predictive maintenance in manufacturing lies in further integration of AI and ML to provide even more accurate predictions and automated decision-making. With advancements in IoT and real-time data analytics, manufacturers will be able to predict failures before they happen with greater precision, leading to less downtime, reduced costs, and enhanced productivity.