Using Big Data and Analytics in Maintenance Industry
Published on : Thursday 03-06-2021
By using the right data analytics solution, maintenance managers will be able to address uncertain downtime.
In recent years big data and analytics have become seemingly ubiquitous across almost every industry. As everyone wants to leverage big data to better their operation, the global big data market is predicted to grow at a CAGR of 10.6% from $138.9 billion in 2020 to $229.4 billion by 2025. However, the maintenance industry is no exception to this trend. Today, maintenance teams are actively embracing the power of big data and analytics to improve performance. But the question arises here is why do they want big data analytics solutions.
One of the major reasons behind it is that most industrial plants have excessive amounts of data stored in their archives, asset management systems, and control and monitoring systems. Companies can turn such an amount of data into actionable information and improve and advance plant operations and maintenance. But this is not as easy as it seems for many plant operators due to a myriad of issues. Due to long operating lifetimes, power plants can lag in the adoption of modern data analytics and other solutions that help improve operations and maintenance. Emphasising the importance and use of big data and analytics software, plants can improve operations and maintenance through root cause analysis, asset optimisation, report generation, and more.
With the help of big data, maintenance can turn into predictive maintenance programs that help lessen downtime and save maintenance costs.
Understanding predictive maintenance
Predictive maintenance is a maintenance technique that uses data analysis tools. It significantly spots anomalies in business operations and possible defects in equipment and processes so that the business can fix them before any uncertainties occur. Predicting the remaining useful life of a piece of equipment or an asset based on real-time data gives companies an unprecedented way to manage and optimize their maintenance resources.
Essentially, predictive maintenance allows the maintenance frequency to be as low as possible to thwart unplanned reactive maintenance, with no costs associated with performing preventive maintenance. This maintenance program not only saves money but also minimises associated risks while saving lives by avoiding catastrophic failures of critical equipment. It does so by using machine data along with other area data that helps the operation to truly understand the health and performance of its machines. Predictive maintenance uses sensors installed and the data to model the equipment performance. Once a model is established, the operation can utilise real-time data to envisage breakdowns in machines.
This information is vitally essential as it gives maintenance managers the ability to perceive a future event and plan an appropriate response accordingly.
Since predictive maintenance is the ultimate maintenance vision for many plant operators, there are numerous benefits this maintenance program offers. These include higher overall equipment effectiveness (OEE), improved productivity for employees, reduced labour and equipment costs, and enhanced safety.
Deriving value from big data and analytics
The data analytics approach nowadays has become the go-to tool for maintenance management in every industry. Since predictive maintenance programs rely on real-time conditions of the equipment, it is a routine activity where equipment items are replaced or repaired at a specific, pre-defined interval, regardless of their condition at the time.
Big data is an evolutionary technology for maintenance operations. While maintenance data is gathered into ERP and other systems, it enables big data analytics. Data can be shared and used between numerous systems, bringing tremendous advantages for maintenance management, planning and operations.
Many manufacturing companies still face challenges predicting when they need to service the equipment. They are also not able to weigh the risks of lost production time against those of a potential breakdown. Traditionally, they address the problem in two ways – reactively and proactively. In reactively, they fix the already existing failures whereas, in proactively, they use past experience to anticipate potential breakdowns. Unfortunately, these approaches are not adequate in today’s data-driven world. If not predicted appropriately, a machine or equipment downtime may be long. As a result, companies not only need to replace a failed part but also order it that gets expensive for them. That also stalls production and increases downtime costs.
Realising the benefits of having big data and analytics, plant managers these days demand and expect more insights, faster, to drive improvements in plant operations and maintenance. Using predictive analytics, these improvements can power asset availability, improving compliance through monitoring of key metrics, or simply greater productivity when accessing contextualised data as input into plans, models, and budgets.
Valmet, a pulp and paper industry company has a strong background in digitalising production processes. The company is leveraging big data to prolong roll life in paper manufacturing operations. By doing so, Valmet has extended roll life by 20%, not only saving money in replacement cost but also lessening downtime as there is less changeover time.
There are many other technological innovations that have dominated headlines in the last few years. These include big data, industrial internet of things (IIoT), open-source, cognitive computing, and the cloud. It is no denying that we live in a consumer and IT world dominated by advanced user experiences. These consumer and IT experiences have closed the required experience gap in terms of usability while providing powerful functionality. Today, users don’t need any technical knowledge or have to master programming to use Google or Amazon Alexa, as these solutions come with easy-to-use experiences.
In this context, it becomes obvious that manufacturers and maintenance managers harness the power of big data and analytics solutions for effective maintenance and processing.
GEMÜ, a leading valves and automation components manufacturer, uses IoT-driven solutions to monitor the performance of manufacturing processes to detect and replace deteriorating components before they fail, improving the efficiency of its manufacturing processes.
The wrap up
The role of big data and analytics is growing in maintenance management. This solution will continue to improve the way manufacturers and the maintenance team performs. By using the right data analytics solution, maintenance managers will be able to address uncertain downtime by turning machine data into actionable information.