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Smart Predictive Maintenance

Our expertise is to help companies to develop and apply smart predictive maintenance concepts for industrial systems to reduce your plant’s maintenance costs & energy expenses while improving environmental sustainability. We help to build novel & proactive intelligent decision support systems for smart predictive maintenance for engineering systems

In the long run, we help your company to be able to offer applications together with services to your customers who manage the other maintenance of assets and reduce their operating costs by using Digitalization and AI approaches. With RISE's collective expertise, we can help your company with technology implementation as well as organisational development. 

Machine health management using a proper predictive smart maintenance deployment is a worldwide-accepted strategy that has become popular in many industries in past decades. Smart predictive maintenance is a modern maintenance technique that advantages multiple technologies and maintenance approaches, including predictive maintenance and condition-based maintenance. These techniques are relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase the profit and safety of the facilities concerned. In addition, smart predictive maintenance is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety, and effectiveness. Decreasing the operational cost of running industrial assets and unplanned services caused by failure and malfunction of the industry can be especially costly.  Therefore, predictive smart maintenance technology should be part of routine maintenance for company assets that absolutely cannot go down. In this way, operation and maintenance managers get pre-information into the future end-of-life of the assets as it runs today. This allows them to plan in advance for repairs or replacement of systems and avoid unexpected downtime, minimise maintenance cost, and uptime of the assets optimised by remotely Inspection & remote diagnosis of the systems.

Excellent and timely maintenance is a key, and with the rise of digitalisation, there is an increased focus on exploiting available data, using enabling technology ( for example, AI, ML, Digital Twins, Big data, etc.) and the digital industry to pursue smart predictive maintenance. A predictive smart maintenance system integrates embedded IoT (for data acquisition and analysis) and artificial intelligence, particularly machine learning techniques for novel reliability & maintainability or RAMS practices and provides effective tools for implementing predictive maintenance to maximise asset performance.

To keep up with the technological innovations within the manufacturing industry in general and maintenance organizations in particular, the implementation of Smart Maintenance can be viewed through the lenses of organizational innovation. Each implementation case varies in practice, but there are some key attributes that are particularly important in the implementation process. There is a need for an implementation strategy that considers these attributes, the current state of the maintenance organization as well as the new technology to be implemented.  

We can provide the answers and information you need within the following:

  • Digitalisation, data collection, AI & smart maintenance approaches
  • Asset health condition monitoring techniques
  • Diagnosis, data acquisition & data analysis
  • Condition-based maintenance and prognostics
  • Decision support matrix  and Improve maintenance strategy
  • Implementation strategy including organizational development
  • Predictive and prescriptive analytics to support proactive decision-making
  • Facilitate to design and develop of Predictive Smart Maintenance
  • Estimate of Remaining Useful Life of systems or components
  • Data-driven approach and Digital model for dynamics systems (AI and Machine Learning Techniques)
  • Physics-based modelling approach (Filtering techniques)
  • A hybrid approach (Physics and Data-drive combine, i.e. Explainable AI and Fusion techniques)
  • Uncertainty analysis/Methods
  • Degradation Modelling for assets
  • Define KPIs
  • LCA and Risk management methodologies
  • Develop the AI-Enabled predictive maintenance digital model for SoS 


Contact person

Madhav Mishra

Senior Scientist

+46 10 228 42 69

Read more about Madhav

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