Predictive maintenance helps companies assess, analyse, and maintain the industrial assets. With the power of IoT, businesses are equipped to pursue better strategies. However, companies must be aware of the issues they may encounter when incorporating a new system.
As with many new technologies, predictive maintenance is not without its challenges. As it is still in the early stages of deployment, companies seeing to integrate predictive maintenance may encounter some of these challenges:
- Multiple Datasets: Predictive maintenance relies on the processing of multiple data sets, especially those related to detection. Multiple modalities are needed for accurate predictions, but combining the sensors required for these predictions requires additional investments, sensors, and data collection, posing new challenges for businesses. Many industrial plants have 8,000 data sensors, with fleets adding up to hundreds of thousands, if not millions of points. Without context and management, the insights coming from this data are meaningless.
- Fragmentation: When multiple sensors and modalities come into play, they create ‘data islands’. With data on different systems, integration can become complex. With cross-platforms semantics, differing systems, and hindered unification in processing, companies may encounter delays in predictions.
- Additional data: Enriching sensor data to enable more accurate predictions can be another challenge faced by companies integrating predictive maintenance solutions. There is no simple way to leverage additional data, making enrichment tricky.
- Lack of tools: Tools are essential in industry, and predictive maintenance – and the large data sets involved – requires advanced algorithms and tools. These are not widely available, and their deployment requires specialist attention.
The primary challenge faced by those adopting Industrial Internet of Things (IoT) solutions is analysing data for efficient deployment. A partnership must be made with engineers, managers, supervisors, assets, operations staff, departments, and the board to balance industrial IoT for appropriate deployment. The goal must be to maximise return on investment, while also ensuring high safety standards, improving productivity, and obtaining meaningful data.
Fortunately, data technologies can overcome many of the challenges faced by businesses seeking to integrate predictive maintenance. Predictive maintenance strategies combine traditional monitoring with modern monitoring, analysis, and data assessments. In industry, this allows for the prediction of machine failures, as well as monitoring that allows for better maintenance and operations.
The Industrial Internet of Things (IoT) has massive scope. With sensors, increased storage capacity, powerful processing, and real-time analytics allowing businesses to access real-time data and turn it into actionable strategy. This data can be used to predictively maintain systems, operations, machines, maintenance, and even improve asset safety and security across remote locations. In turn, productivity is increased, and costs are reduced. With the correct application, and the right team aiding integration and deployment, companies can overcome the challenges associated with integration and learn how to best utilise their new system.
Managing data, integration, maintenance, and analysis is difficult. Business is rife with challenges, but the team at MOQdigital are here to help. As experts in predictive maintenance, we want to help you make the most of your assets.
Get in touch today to find out more.