The market for predictive maintenance is expanding fast as a result of the use of machine learning algorithms, advanced analytics, and Internet of Things technology. It has become an important strategy for organisations looking to optimise maintenance, cut expenses, and improve efficiency.
Predictive maintenance makes use of analytics and real-time data to identify potential issues. By monitoring equipment health, analyzing data patterns, and applying machine learning, organizations are able to predict maintenance needs, schedule activities during planned downtimes, and prevent unexpected breakdowns.
As technology advances and the advantages of predictive maintenance become more visible, the market is expected to increase at a CAGR of 30.6% by 2026. Organisations are able to attain higher efficiency, reliability, and productivity, thanks to this upward trajectory.
Industries are gradually realising the significance of predictive maintenance in this age of rapid digital transformation. Predicting potential issues and planning maintenance tasks ahead of time can greatly cut downtime, extend the life of an asset, and save maintenance expenses. However, what if we could take this one step further by integrating human observations with traditional data sources?
The scope of predictive maintenance has grown beyond traditional methods, now including a diverse variety of assets, including sophisticated systems and machinery. Predictive maintenance uses machine learning, real-time data, and predictive analytics to predict issues, in contrast to traditional maintenance methods that depend on fixed schedules or reactive responses.
Predictive maintenance monitors the performance of equipment by using real-time data from sensors. It analyses data to determine trends and patterns in order identify potential equipment failure and resolve an issue before it occurs.
In addition to using sensor and automated system data, we may also leverage the rich and valuable data that field-based technicians observe on a daily basis. We can take useful insight from a technician that he/she sees, feels or hears on the ground, to add context to what could be going on inside the asset.
What if these technicians could efficiently upload audio and video files directly from their site to assess asset performance? By merging this data with operational sensor data, Building Automation Systems (BAS), and unstructured data from Computerized Maintenance Management Systems (CMMS), we might discover new and potentially transformative insights about asset performance. This invaluable information can serve as a repository for other technicians, ensuring that an organisation never workforce knowledge when their technicians leave the organization.
The integrated data, bringing together human observation with automated systems, creates an overview of the asset’s performance and condition. This data can be processed to find patterns, correlations, and trends using machine learning, artificial intelligence, and advanced data analytics algorithms. Large datasets may be analysed by AI-powered algorithms, which can identify trends and generate accurate asset health predictions. Organisations can shift from reactive to proactive maintenance by utilising these technologies, which will maximise asset performance and prevent breakdowns.
Large volumes of data can be processed by AI and machine learning algorithms, which helps predictive maintenance systems identify abnormalities and patterns that human operators would miss. With the use of these technologies, organisations may more accurately predict asset breakdowns, plan maintenance schedules, and distribute resources more effectively.
In addition, natural language processing (NLP) algorithms can extract useful insights from unstructured data in the CMMS. These algorithms can identify certain visual or auditory patterns that signify the initial stages of failure.
Predictive maintenance can benefit greatly from the insights extracted from unstructured data, such as maintenance logs, sensor readings, and technician reports. Relevant information can be extracted from unstructured data sources using AI-based methods like text mining and natural language processing. Organisations may improve their predictive maintenance models and make data-driven decisions by putting these insights to use.
This approach doesn’t simply lead to better asset maintenance and longer asset life. It offers a considerable financial advantage as well.
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, operations team, and the organization.