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