Predictive Maintenance 4.0

Predictive maintenance for industry 4.0 is a method of preventing asset failure by analyzing production data to identify patterns and predict issues before they happen.

Until now, factory managers and machine operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime. In addition to consuming unnecessary resources and driving productivity losses, half of all preventive maintenance activities are ineffective.

It is not a surprise therefore, that predictive maintenance has quickly emerged as a leading Industry 4.0 use case for manufacturers and asset managers. Implementing industrial IoT technologies to monitor asset health, optimize maintenance schedules, and gaining real-time alerts to operational risks, allows manufacturers to lower service costs, maximize uptime and improve production throughput.

Level 1

Visual inspections: Periodic physical inspections; conclusions are based solely on inspector’s expertise.

Level 2

Instrument inspections: Periodic inspections; conclusions are based on a combination of inspector’s expertise and instrument read-outs.

Level 3

Real-time condition monitoring: Continuous real-time monitoring of assets, with alerts given based on pre-established rules or critical levels.

Level 4

PDM 4.0: Continuous real-time monitoring of assets, with alerts sent based on predictive techniques, such as regression analysis.

For predictive maintenance to be carried out on an industrial asset, the following base components are required:

1. Sensors

Data-collecting sensors installed in the physical product or machine

2. Data communication

The communication system that allows data to securely flow between the monitored asset and the central data store

3. Central data store

The central data hub in which asset data (from OT systems), and business data (from IT systems) are stored, processed and analyzed; either on-premise or on-cloud

4. Predictive analytics

Predictive analytics algorithms applied to the aggregated data to recognize patterns and generate insights in the form of dashboards and alerts

5. Root cause analysis

Data analysis tools used by maintenance and process engineers to investigate the insights and determine the corrective action to be performed

Production asset data is streamed from the sensors to a central repository using industrial communication protocols and gateways. Business data from ERP and MES systems, together with manufacturing process flows, are integrated into the central data repository to provide context to the production asset data. Then, predictive analytics algorithms are applied to provide insights for reducing downtime, which are investigated using root cause analysis software.

Manufacturers and their customers get a range of business benefits from thinktank predictive maintenance.

The advantages of PDM 4.0 include:

  • Reduced maintenance time
  • Increased efficiency
  • New revenue streams
  • Improved customer satisfaction
  • Competitive advantage