Predictive
Predicting ASSET FAILURE and QUALITY DRIFTS
Intelligent industrial processes
Challenges and Pressures
- Production or normal running interruptions due to faults, downtime or failures
- Maintenance programmes not optimised, reactive not proactive
- Data Analysis time consuming and require data expertise
- Skill shortages
- Ever increasing sustainability targets and goals
Solution
- Direct asset connectivity
- Raw data and performance metrics breakdown visualised in detail
- Alerts and Notifications generated by ML applications
- Trained ML models converting data into insights
- Develop and Deploy new and bespoke ML models
- Expert advice to implement changes
Benefits
- Improve reliability and availability of your equipment
- Minimise downtime and reduce maintenance costs
- Increase the lifespan of your equipment
- Enhance the overall performance of your industrial assets
- Increase the value provided to customers through proactive maintenance of your products
- Develop a servitisation business model to generate new revenue streams
Features
- Connect directly to sensors and PLCs for real time, rich, high frequency data
- Real time visualisation of metrics and KPIs
- Performance breakdown of all the assets and equipment used in the facility
- Out of the box analytics to convert raw data into actionable insights
- Further tools to create your own dashboards, analytics and Machine Learning models
Smart Monitoring of Industrial Chillers
Reducing Failures on 1,000’s of Industrial Chillers
- Manufacture 4,000 of industrial chillers per year
- Supplying to data centre, healthcare, pharmaceuticals and retail markets
- Using Smartia Predictive to remotely monitor chillers and predict optimum maintenance cycles
- Early refrigerant leak detection saving ~£5,000 per unit per year
Our Projects
Helping industrial companies harness the power of AI. Our consultative approach to automating and enhancing industrial operations allows us to deploy customised AI solutions that deliver tangible improvements to production efficiency, machine uptime, maintenance scheduling and quality control applications.
Predictive Maintenance
Advance warnings 9 months before potential failures and £150k operational savings per turbine.
Predictive Maintenance
Predictive Failure Detection
Predict 90% failures before they occur saving over £100k per composite part and reducing energy and material waste.
Predictive Failure Detection
Automated Defect Detection
Automated defect detection reduced the man power required and therefore reduced the associated costs substantially. It also allowed staff to focus on other business critical tasks.
Automated Defect Detection
Automated Quality Inspection
An automated product quality inspection system which has increased quality levels and saves £20m per plant annually.
Automated Quality Inspection
Inventory Optimisation
£1M savings in a single quarter, and potentially achieving £6M savings within 18 months.
Inventory Optimisation
Fault Detection
Computer vision based fault detection, automating the inspection process and increasing coverage by over 3 fold.
Fault Detection
Scrap Reduction
Detecting scrap in the single grain crystallisation casting process using regression modelling and advanced analytics.
Scrap Reduction
Smart Robotics Object Detection
Robotic fulfilment rig that demonstrates how robotics and machine learning can be used to help support staff.
Smart Robotics Object Detection
Ultra Precision Manufacturing
Switched from the regular cutting tool replacement process at every 200 products to a monitored based system that extended the life of a cutting tool by up to 3 times.
Ultra Precision Manufacturing
Preventive Maintenance Support
Health Indicator application provides early alerts that equipment is degrading and likely to need maintenance.
Preventive Maintenance Support
Energy Usage Optimisation
Power usage effectiveness (PUE) is an important industry metric for measuring the energy efficiency of a data centre. Optimising this can provide huge cost savings of up to 20% in this case.
Energy Usage Optimisation
Tool Life Optimisation
Condition-based tool replacement using data and machine learning to inform decisions. Leading to a reduction in cost and extending the life of the tools.
Tool Life Optimisation
AI-based Security at the Edge
Smartia’s MAIO will enable AI at the edge, supporting privacy-preserving distributed learning. Use of AI for cyber security to detect attacks and improve the security of large scale IIoT systems.
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