Predictive Maintenance

Predictive Maintenance

Manufacturers have used a time-based approach to equipment maintenance for many years. They used to consider the age of the machinery when planning the maintenance schedule. The older the equipment, the more frequent the maintenance procedures must be performed. According to ARC group statistics, only 18% of equipment fails due to machinery age, while 82% fail at random. It demonstrates that a time-based approach is inefficient. Manufacturers use Industrial IoT predictive maintenance and data science to avoid ineffective maintenance routines and the costs that come with them.

This means that in the coming years, more business problems, including those unrelated to machine maintenance, will be reorganized as predictive maintenance.

Predictive maintenance and IoT solutions, in particular, will solve problems we never considered — once we learn to creatively remake those problems in a way that can be solved using IoT data and predictive analysis. Today, we’ll look at predictive maintenance in the Industrial IoT and how these advancements are increasing industry efficiencies.

Industry predictive maintenance 4.0 is a method of preventing asset failure by monitoring production data to identify patterns and predict problems before they occur. Previously, factory managers and system operators performed scheduled maintenance, other processes, and repaired machine parts on a regular basis to avoid downtime.

Predictive maintenance has quickly emerged as a key Industry 4.0 application for manufacturers, plant managers, and asset managers. Manufacturers can reduce service costs, increase uptime, and improve production throughput by implementing IIOT technology to analyze asset nature, optimize maintenance schedules, and gain real-time alerts to operational risks.

Predictive maintenance goals are divided into two outcomes:

  • Improving Production Efficiency: Using machine readings, production efficiency can be improved by increasing the time machines are running through predictive maintenance or predicting the number of goods that will pass or fail quality inspection. By addressing problems before they cause equipment failures, manufacturers can reduce maintenance costs, extend equipment life, reduce downtime, and improve production quality.

 

  • Improving Maintenance Efficiency: By anticipating failure, maintenance can be planned before it occurs. This improves the overall system and equipment maintenance efficiency.

The following components are required for predictive maintenance to be carried out:

  • Sensor Data Collection Sensors Installed in the Machine
  • Data Transmission
    • The system that allows data to safely travel between the analyzed asset and the stored data.
  • centralized data storage
    • It is the central data store where operational technology asset data and information technology business data are stored, monitored, and processed for future operations.
  • Predictive modeling
    • Analytics algorithms are used to identify patterns in large amounts of data and then generate insights in the form of alerts.
  • Investigation of the root cause
    • Process engineers use this tool to validate the insights and determine the appropriate preventive action.

Using various IoT protocols and gateways, production asset data is flowing from sensors to a central store. The business data from ERP and MES systems, along with the manufacturing process, are combined into a central data store that provides context for the production asset data.

Then, predictive analytics algorithms are used to provide insight in the form of alerts or dashboards in order to reduce downtime. Then it investigates using root cause analysis software.

Machine operators must map the parameters of failure for machines and create a blueprint for their connected system, which includes sensors and manufacturing assets, business systems, communication protocols, gateways, predictive analytics, cloud, and visualization, in order to effectively apply a predictive maintenance system. Predictive analytics are applied to machine data and system blueprint data to estimate the conditions of upcoming failure.