As we tread along these uncertain times amid the global pandemic, the ARC Industry Forum Asia, titled: Accelerating digital transformation in a post-Covid world, was virtual and ABB participated as a Gold Sponsor. The conference came at a time during the global health and economic crisis and we witnessed an unprecedented level of industrial innovation as organisations continued to adapt and make changes while driving operational excellence, including asset reliability.
At the Forum, Anindya Chatterjee, ABB’s global head of Value Engineering and Data, gave a presentation that primarily focused on return on asset (ROA) reliability through information (IT), operations (OT) and engineering technologies (ET), powered by machine learning (ML). Further, he unveiled the ABB Ability Genix APM (asset performance management) solution that brings together the combined power of industrial analytics and industrial artificial intelligence (AI) as an enterprise-grade platform and suite to transform productivity and operational excellence. Let’s delve deeper into his presentation.
Decoding ROA reliability
According to Chatterjee, return on asset reliability refers to a practice of maximising the earned value of each asset by analysing both direct and indirect losses from its ideal performance. It derives potential opportunities for improvement in operation and maintenance strategies and assists in long-term decision making for critical capital investments. But prompt decision-making and operational excellence will not be possible without keeping data at the centre of digital transformation as companies join the bandwagon to implement automation solutions and systems that not only promise to optimise productivity but drive sustainability.
Harnessing the power of data
There is a deluge of information to help with optimising operations or maintenance reliability, but to take a firm a step ahead of peers and make it more efficient and facilitate a faster and intelligent decision-making process, machine learning, advanced analytics and similar solutions must be deployed above the data layer in a seamless manner to drive return on assets from a reliability perspective. The challenge is how to best utilise data to boost productivity, reduce costs and improve performance across the whole digital value chain. To achieve that Chatterjee emphasised the importance of driving asset management while walking the Industry 4.0 path.
Industry 4.0 drive for asset management
ABB, with its knowledge and experience in this area, has developed a solution driving Industry 4.0 for asset management. In a continuous journey to achieve operational excellence, the reliability transformation strategy has moved from reactive to preventive to predictive and each step along that strategic path has its own strengths and weaknesses. Surveys reveal that as we move towards more predictable operations, predictive maintenance is garnering more attention and investment. Speaking about the digital program for asset management, Chatterjee said that it is all about continuous optimisation and improvement.
Driving operational excellence
The highlight of Chatterjee’s presentation was the ABB Ability Genix Industrial Analytics and AI suite that brings together data with domain knowledge across diverse industries, technology and digital capability for maximum impact and driving key business outcomes, like helping improve operational excellence, asset integrity and performance, sustainability, safety, energy efficiency and supply chain optimisation, among others.
With ABB Ability Genix, decision making can be accelerated to predict and optimise asset, plant and enterprise performance and get actionable insights customised to operating conditions and needs. Additionally, value can be derived from data by applying analytics across multiple processes and sites and cutting across functions.
The suite embodies ABB’s strategy to build a platform that is flexibly configurable, can be deployed seamlessly and securely across the edge, fleet, plants and the enterprise and it can be on-premises or hosted, on private or hybrid and on a single or multi-cloud platform according to a company’s specific needs. The key steps: integrate, contextualise, model, analyse, deliver and optimise.
Anomaly detection is a major objective of predictive maintenance. The data enables learning and insight to avoid future failures for different industrial scenarios and failure modes. It is useful to utilise different AI or ML approaches to identify faults before they impact the asset’s health and performance. Return on assets with ABB Ability Genix APM drives ‘availability safely’ by integrating performance, maintainability and reliability.
The predictive maintenance feature consists of the following modules:
Asset Performance Centre: provides functionality for an holistic asset visualisation and analysis based on operational, constructional, derived and predicted parameters, as well as standard asset KPIs related to performance, health, maintenance schedules and asset lifecycle cost. How it works: integration, contextualisation, asset configuration and overall performance monitoring.
Predictive Maintenance Centre: provides advanced fault prediction using customised rule-based or AI or ML-based asset models including recommendations, visualisation and events management.
Asset Life Assessment: provides detailed assessment opportunities by analysing equipment history through different perspectives, such as design, operation and maintenance history to generate a plan for asset replacement or life extension.
While moving towards a post-Covid world, empowering customers to unlock value from data and driving better business results is crucial. ABB’s ROA reliability solution may just be the answer you are looking for.
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