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Keywords: Asset Management, Artificial Intelligence (AI), Industrial AI, Predictive Maintenance (PdM), Asset Performance Management (APM), Analytics, Machine Learning (ML), Generative AI (GenAI), Large Language Models (LLMs), Large Multimodal Models (LMMs), ARC Advisory Group.
Overview
Asset performance management (APM) has become a popular term for solutions that helps asset-intensive enterprises extract the most value from their asset investments. The current generation of APM solutions leverages new technologies to enable more effective operations and avoid unplanned downtime. APM tools typically include maintenance management, asset health monitoring condition monitoring, predictive maintenance, and often involve technologies for data collection, visualization, and analytics.
APM has become a primary enabler of digital transformation for asset management among industrial companies. Modern APM combines traditional asset management practices with new digital technologies for transformation advances in reliability, maintenance execution, and business performance. Artificial intelligence (AI) is key among these newer digital technologies.
AI for Asset Performance Management
AI is not a new technology for the industrial world. Broadly speaking, AI refers to the computer systems and programs that can simulate human intelligence and perform tasks to learn patterns, solve problems, and make decisions. Industrial AI, a subset of the broader field of AI, refers to the application of AI technologies (including Generative AI) in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, enhanced customer service, and business outcomes. Industrial AI is already being leveraged by industrial manufacturers in various industrial applications including APM.
Machine learning (ML), a subfield of AI, has found several use cases in the industrial world. ML specifically focuses on algorithms that can learn from a wide range of data to make predictions and help with decision making. These algorithms use distinct types of statistical techniques to enable data analysis and make more accurate predictions and decisions.
PdM Can Simplify APM
For the industrial manufacturers, unplanned downtime continues to be a major concern. Early detection of potential asset failure can be extremely helpful to organizations trying to minimize unplanned downtime.
Predictive maintenance (PdM) employs advanced modeling and machine learning (ML) technologies, to analyze hundreds of process parameters over time, as well as compare these to historical asset data. This helps manufacturers estimate wear and degradation of assets or its parts and forecast asset failure in advance. This helps improve lead times, providing operators with better information sooner so that they have more time to address the issues to avoid imminent asset failures.
While manufacturers understand the benefits of PdM, not all manufacturers are able to reap its benefits. Successful implementation of PdM programs remains a major challenge. For a successful PdM program, it is imperative that the ML algorithms are trained on clean data, the right amount of data, and the right type of data.
GenAI to Address PdM Challenges
Data availability, quality, and reliability is a major challenge for most manufacturers. It is common for many manufacturers to have missing, wrong, or incomplete information about equipment, maintenance tasks, or operation processes. Data gaps, periods where data isn’t captured at all or have inconsistencies add noise to the data and significantly lowers data quality.
Lack of stringent data standards and poor system integrations are some of the major reasons that lead to this data gap. PdM algorithms heavily depend on data quality. If PdM algorithms are prepared with poor or limited data, the quality of algorithms is compromised, which can lead to unreliable models, which in turn means more false alarms or missing key alarm events. This further leads to diminishing trust in PdM technology and hence limited or no success with PdM initiatives. Hence, having the right data is key to achieving success with PdM programs.
Another subfield of AI, Generative AI (GenAI) can now be leveraged to tackle this data gap challenge. GenAI focuses on producing new, original content by learning from existing data. This branch of AI gained major popularity after the massive success of ChatGPT. Various organizations in different types of industries are now looking to explore GenAI applications. One interesting application of GenAI is to help generate synthetic data. This capability can be of tremendous help in addressing the data gap challenges regarding PdM initiatives.
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