Harnessing Human Observations and AI: The Future of Predictive Maintenance

The predictive maintenance market is rapidly growing due to the adoption of advanced analytics, IoT technology, and machine learning algorithms. It has emerged as a key strategy for organizations to optimize maintenance, reduce costs, and improve efficiency.

Predictive maintenance utilizes real-time data and analytics to identify potential failures in advance. By monitoring equipment health, analyzing data patterns, and employing machine learning, organizations can accurately predict maintenance needs, schedule activities during planned downtimes, and prevent unexpected breakdowns.

As technology advances and the benefits of predictive maintenance become more evident, the market is projected to grow at a CAGR of 30.6% by 2026 . This upward trajectory empowers organizations to achieve higher efficiency, reliability, and productivity.

In an era of accelerated digital transformation, the power of predictive maintenance is gradually being acknowledged across industries . The ability to predict potential failures and schedule maintenance activities proactively can significantly decrease downtime, improve the asset life cycle, and reduce maintenance costs. But what if we could take this a step further by integrating human observations with traditional data sources?

Expanding the Scope of Predictive Maintenance:

The scope of predictive maintenance is expanding beyond traditional methods, now encompassing a wide range of assets, including complex systems and machinery. Unlike conventional maintenance approaches that rely on fixed schedules or reactive responses, predictive maintenance leverages real-time data, predictive analytics, and machine learning to anticipate failures.

Integrating Human Observations with Automated Data Sources

Predictive maintenance uses real-time data from sensors in equipment to keep track of equipment functioning. It analyses data to identify trends and patterns in order to predict equipment failure and resolve an issue before it occurs.

In addition to data from sensors and automated systems, we can also use the rich, valuable data that human technicians observe daily on the ground. We can take valuable insight from a technician that he/she sees, feels, or hears on the ground, to add context to what might be going on inside the asset.?

What if these technicians could securely upload audio and video files directly from their worksite to assess asset performance? By combining this data with operational sensor data, Building Automation Systems (BAS), and unstructured data from Computerized Maintenance Management Systems (CMMS), we can uncover new and potentially transformative insights about asset performance. This valuable data can act as a repository for other technicians and an organization would never lose their workforce knowledge when their technicians leave the organization.

Leveraging Advanced Technologies:

The integrated data, combining human observation with automated systems, create a holistic view of the asset’s performance and condition. Using advanced data analytics, AI, and Machine Learning algorithms, this data can be processed to identify patterns, correlations, and trends. AI-powered algorithms can analyze large datasets, identify patterns, and generate precise predictions about asset health. By leveraging these technologies, organizations can move from reactive to proactive maintenance, preventing failures and maximizing asset performance.

The Role of AI and Machine Learning in Predictive Maintenance

AI and machine learning algorithms can process vast amounts of data, enabling predictive maintenance systems to detect subtle patterns and anomalies that might go unnoticed by Human operators. These technologies empower organizations to predict asset failures with higher accuracy, optimize maintenance schedules, and allocate resources more efficiently.

Certain visual or audio patterns that indicate the onset of failure can be recognized, and natural language processing (NLP) algorithms can extract valuable insights from unstructured data in the CMMS.

Extracting Insights from Unstructured Data

Unstructured data, such as maintenance logs, sensor readings, and technician reports, contain valuable insights for predictive maintenance. AI-based techniques, including natural language processing and text mining, can extract meaningful information from unstructured data sources. By utilizing these insights, organizations can enhance their predictive maintenance models and make data-driven decisions.

The Cost-Benefit Aspect:

This approach doesn’t just result in better asset maintenance and longer asset life. It also has a significant financial benefit.?

• By reducing the need for physical infrastructure and more efficient maintenance practices, capital and operational expenditures can be decreased.?
• Essentially, predictive maintenance is not just about avoiding asset failure, but also about improving overall operational efficiency and the bottom line. Proactive maintenance practices lead to a longer asset lifespan, reducing the frequency of costly repairs or premature replacements.
• With the ability to accurately predict maintenance needs, organizations can optimize their inventory management processes. By stocking only the necessary spare parts and consumables, organizations can reduce excess inventory, minimize carrying costs, and eliminate obsolete or underutilized inventory.
• Predictive maintenance also helps identify equipment inefficiencies and malfunctions that can lead to excessive energy consumption. By rectifying such issues promptly, energy usage is optimized and reduces utility costs.
• One of the major benefits is retaining workforce knowledge and using this on-ground knowledge repository to understand the asset better and help technicians to make informed decisions.

The Future of Asset Maintenance and Management:

The future of asset maintenance and management lies in harnessing the power of human observations and AI technologies. Predictive maintenance is a lot more within reach than we might think. By leveraging the power of human observations, traditional data sources, and cutting-edge technology, we can transform the way we approach asset maintenance and management. It’s a win-win for everyone - technicians, the operations team, and the organization.

At?Xempla - Decision Support System for Enterprise Asset Management , we're powering predictive maintenance among other use cases like FDD, real-time monitoring, and energy management for some of the world's best in the facility management business. Companies like Serco, Sodexo, Emcor UK, and Engie Solutions Middle East have already empowered their O&M teams and achieved?incredible results ?with our Asset Performance Management software.

Ping me on?LinkedIn ?to start the conversation (transformation)!??

If you found this article helpful, join 2k+ professionals reading the?Forever Forward Newsletter ?and get notified of more such insights and strategic intelligence. Break free from the status quo and help shape a future that's more inclusive and sustainable for everyone in facilities management, asset operations, and maintenance.