IML Services: Smart Factory and Predictive Maintenance

Implementing real-time remote monitoring and intelligent control over IoT network with enhanced security, currently new automation models by digital transformations are redefining industry, addressing current challenges and paving the way for a more efficient and sustainable future.

With the growing need for energy with an associated increase in carbon emissions and impact on the transition to net zero emission target, industry sectors are now under great pressure to significantly improve energy efficiency and reduce emissions by adopting technological innovations to deliver sustained solutions to many challenges relating to climate change.

The response to the current energy crisis and concerns being one of the major global issues, has reinforced the need for technology enablement to support large scale digital transformation and models to adapt rapidly to implement real-time remote monitoring and accelerate predictive analytics and optimal control of industrial systems and to build smart manufacturing/factory with more sustainable and energy-efficient operation of structures and processes of industrial manufacturing, where factories are equipped with advanced sensors, IoT communication networks and embedded software to collect and analyse data, allowing for better decision making by deploying artificial intelligence (AI) on production lines (both at edge and cloud), which is reshaping the manufacturing landscape.

Through continuous monitoring of processes and tracking everything that is relevant to operations such as sensor data from vibrations, temperatures and noise measurements, predictive maintenance taking timely actions based on outputs from diagnostics, is enabled by data-driven decision making, where by proactive identification of equipment issues and potential failures, actions can be taken to prevent predicting failures or degradation, thus reducing downtime of industrial machines and engines significantly, and ensuring that a production line is running as efficiently as possible and to reduce waste and losses.

Combination of AI with expert knowledge can be enriched dynamically with acquired data, and can provide support maintenance decision making. By accurately predicting when an engine will fail and when a replacement order should be placed, system failures can be prevented without unnecessary interruptions. As a result, optimal operations of industrial processes can be achieved with improved energy efficiency and maximised productivity that benefit from the digital transformation by delivering AI-based data-driven systems and products that integrate the advancements in data sensing, processing, communication, and AI optimising processes via learning through interactions.