PdM Use Cases

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Predictive Maintenance - Use cases

Here are some case studies of successful Predictive Maintenance implementations across a range of industries globally

Disclaimer: The links below are external to The Data Lab website and are provided for illustration purposes only.
Inclusion here does not represent an endorsement by The Data Lab.

Manufacturing

Company: Volvo – Use Case

Assets: Vehicles (Trucks)
Technology: IBM SPSS, IBM Hybrid Cloud
Benefits: Diagnostic time 70% reduction, Repair Time 20 % reduction

Volvo Group Trucks invested in a new predictive analytics platform using IBM SPSS for vehicle information due to a growing business need for predictive maintenance to fulfil up-time commitments. It transformed its use of vehicle data from reactive to predictive analysis.

By being able to monitor the truck’s usage and the current status of the vehicle’s various key components, it is possible to tailor maintenance to individual truck level PdM and also to predict component failure while the truck is on the road or in the shop

Company: Unnamed – Use Case

Assets: Rubber & Plastic manufacturing plant
Technology: eMaint CMMS
Benefits: Increased production up-time, operational efficiency

A maintenance culture shift inspired by an integrated Computerised Maintenance Management Software (CMMS) improved the planned work and the bottom line at a 100-year-old rubber and plastics plant. The 750,000-square-foot plant houses more than 600 systems and subsystems maintained by a crew of less than 50 people. With asset ages ranging from 20 to 80 years, breakdown work orders out-numbered planned maintenance work orders by a large margin.

Using approaches such as thermal imaging, vibration detection, condition monitoring alongside the CMMS enabled the plant maintenance activity to be successfully incrementally transformed.

Oil & Gas

Company: Chevron – Use Case

Assets: Oil and gas end-to-end production equipment
Technology: Microsoft Azure
Benefits: Significant cost savings and transformational new solutions in exploration, midstream logistics, retail operations and the management of thousands of oil wells around the world

Chevron have signed a multi-year partnership with Microsoft Azure (Partnership Announcement) to enable their efforts to digitise their oil fields and accelerate deployment of new technologies that can increase revenues, reduce costs and improve the safety and reliability of operations.

Implementing Predictive Maintenance across Chevron’s oil fields and refineries will enable thousands of pieces of equipment with sensors (by 2024) to predict exactly when equipment will need to be serviced.

Company: Schneider Electric – Use Case

Assets: Pumps used in oil & gas  production
Technology: Azure Machine Learning and Azure IoT Edge
Benefits: Operational efficiency; reduced unplanned downtime; improved safety

Schneider Electric set out to solve the challenge of remote asset management for the oil and gas industry. When connected assets are distributed across a country or around the world, edge analytics makes remote asset management easier by putting application logic onsite.

Food & Drink

Company: Lamonica’s Pizza Dough – Use Case

Assets: Refrigeration Units
Technology: Fluke Power Monitor
Benefits: Reduced unplanned downtime

Remote condition monitoring (CM) — the practice of using sensors and software to monitor performance abnormalities in assets — is emerging as a business-critical activity in the food processing industry. CM can be seen as a step beyond Predictive Maintenance.

This case study covers how PdM and CM have been used in the production of ready-to-bake raw pizza dough.

Company: InBev – Use Case

Assets: Bottle filler carousel bearings
Technology: Emerson AMS Machine Manager
Benefits: Minimised production downtime

InBev implemented PdM to minimise downtime in their 24/7 production and bottling facility.

Maintaining a variety of specialised machinery across the brewing, bottling, packaging and shipping processes demands precise maintenance planning and equipment monitoring.

Transportation

Company: VR Group – Use Case

Assets: Railway rolling stock
Technology: SAS Analytics; SAS AI Solutions
Benefits: Cost reduction; improved customer safety and experience

VR Group, the state-owned railway in Finland, turned to SAS Analytics and the Internet of Things (IoT) to keep its fleet of 1,500 trains on the rails and provide a better, safer experience for its customers.

To reduce costs and maximise up-time, VR Group wanted to move from a traditional maintenance approach that focused on replacing parts as needed. They developed a predictive maintenance program that focuses on monitoring the condition of parts at all times.

Company: Downer / NWS Government – Use Case

Assets: Railway rolling stock
Technology: Azure IoT HubAzure Data Lake Storage; Azure Service Fabric
Benefits: Improved reliability and performance; improved customer safety and experience; cost reduction

Since 2016, the NSW Government has deployed a fleet of Waratah Series 2 trains under its Sydney Growth Trains Project. These trains provide more passengers with improved safety and comfort due to enhanced air-conditioning systems, more CCTV cameras and improved accessibility alongside exceptional performance in terms of reliability and availability.

As each Waratah train pulls in and out of a Sydney station, more than 300 Internet of Things (IoT) sensors and almost 90 cameras are silently capturing data and recording video.

Every ten minutes 30,000 signals are sent from the train to Downer. Those 30,000 signals represent the train’s digital DNA.

Utilities

Company: Hitachi Wind Power Ltd. – Use Case

Assets: Wind turbines
T
echnology: Hitachi Lumada
Benefits: Improved performance; improved safety; reduced downtime

A use case explaining how wind power has been commercialised in Japan despite the severity of Japan’s weather and natural environment. Predictive maintenance plays a key role in this overall solution.

Company: EDF Energy – Use Case

Assets: Gas Turbine Power Stations
Technology: Emerson AMS Suite; SAP Enterprise Asset Management
Benefits: Reduced downtime; significant cost savings

Power Generation
EDF Energy have reduced the numbers of very costly trips at their gas turbine power stations through improved asset management and predictive maintenance.