Companies today have more access to data than ever before in history. This is expected to increase as the cost of the core connectivity devices become cheaper and more accessible worldwide. From HR to accounting, employee training to product manufacturing and delivery, businesses are looking to continue leveraging the data at their disposal to make their internal process better, faster, and more efficient.
As expected, they are already using these technologies to address one of the most challenging aspects of running an organization that owns physical assets - efficient maintenance management. Of particular interest is the fact that these technologies allow communication from machine-to-machine (M2M) and from machine-to-humans (M2H). However, the fact that machines are now able to think intelligently, learn, teach themselves, make decisions and respond like (or even better) than humans, has launched a never-before-seen opportunity to improve recurrent maintenance issues.
Today, there is profound interest in the ways to use technologies like Artificial Intelligence (AI), Machine Learning, Virtual Reality (VR), and the Industrial Internet of Things (IIoT) to reduce costs and improve asset optimization and workers' safety.
The following is a look at how the face of maintenance is changing with the introduction of said technologies.
Artificial Intelligence in Maintenance Management
The cost of unplanned equipment downtime is staggering. Take the manufacturing sector for instance - the International Society of Automation (ISA) estimates that manufacturers lose $647 billion globally every year to downtime.
Over the years, the shortcomings of time-based maintenance have become obvious. A Boeing study shows that up to 85 percent of equipment fail despite regular calendar-based maintenance. Predictive maintenance came as a result of the need to improve on these shortcomings.
Artificial Learning and Predictive Maintenance: How It Works
AI software:
- collects information from sensors,
- monitors anomalies and patterns,
- uses that information to request for human intervention.
For predictive maintenance, there are two ways AI monitors the information coming out of assets:
Anomaly: The system reads data generated from the equipment and picks up any variations from normal running conditions.
Failure: This focuses on data patterns to detect a potential failure based on similarities with predefined failure modes.
Both of these generate alerts to let the maintenance team know that there’s a need to investigate the situation. When combined, they form the basis of advance maintenance strategies like condition-based maintenance and predictive maintenance.
Other Maintenance Applications for AI
Housekeeping - Robots are being used to perform a number of cleaning functions in large warehouse facilities and hotels. Through machine learning, these robots can teach themselves and learn through trial and error without human interaction.
It’s an interesting alternative to human labor especially for high-risk cleaning activities like cleaning at heights and duct-cleaning.
Inventory Management - Automated inventory management means organizations can minimize, or eliminate, the need for manual inventory checking. This removes the common problem of human error and time wasted while checking large stocks of items.
Fleet Management - Artificial intelligence has become a game-changer for fleet management. The growing adoption of AI means fleet and maintenance managers can now achieve more control over the most pressing challenges they face especially with regards to fuel management, vehicle maintenance, logistics, unauthorized use of vehicles, safety and driver performance and tracking.
The possibilities are still being developed but it’s already obvious that this technology delivers better-managed fleets, considerable cost savings, and improved productivity.
Virtual Reality in Maintenance
Companies are using virtual reality technology in their maintenance efforts majorly for employee training. At the forefront of this trend are manufacturers and major aviation giants including Air France, Airbus, and Boeing.
It’s easy to understand why. For example, the manufacturers want to empower their workforce and plant-operators to maintain factories of the future (FoF). But, the assets in smart factories are typically extremely expensive and sensitive.
Instead of hiring experts or designating more experienced staff to train new entrants every time, these businesses are creating unique training experiences that are accessible just by wearing a VR headset or operating AR apps on tablets and smartphones. The trainee is able to interact with a customized simulated environment and experience a first-person view.
An added advantage is that these training are accessible on-demand basis 24/7 and for multinationals, their staff can train themselves from any of their facilities anywhere around the globe. Or even from the comfort of their homes in their free time.
What Does the Future Hold?
Going into the future, the implications of AI for maintenance in whatever industry will likely be in the form of more automation and responsibilities for the machines rather than humans.
The thought and decision-making process of machines, especially robots, will continue to advance with several benefits such as carrying out work in dangerous or hazardous environments, the use of self-driving vehicles for better supply chain management (SCM), and monitoring humans to prevent fatigue-related accidents.
No one knows exactly what it’s going to look like but definitely, maintenance in tandem with artificial intelligence will be better and more efficient than what was available say 50 years ago.