What is Predictive Maintenance?

Predictive Maintenance (PdM for short) is a asset maintenance strategy that leverages system data to predict failure and intervene at the optimal time given the constaints of the business. It relies heavily on a set of techniques that are specialised in detecting early signs of failures in complex systems and predicting how long these systems can continue to operate before failing. These techniques constitute of algorithms that analyse the real-time data of a system and abstract away information into conditional indicators that are reflective of system health.

Remaining Useful Life is the Holy Grail

Maintaining critical systems and equipment is a never-ending challenge for businesses across industries. Unexpected breakdowns can lead to costly downtime, lost productivity, and dissatisfied customers. However, there is a Holy Grail in the realm of maintenance strategies that stands to revolutionise the way maintenance is approached: Remaining Useful Life (RUL) estimation. RUL is a core component of the Predictive Maintenance pipeline. It empowers businesses to predict when equipment will require maintenance or replacement before failures occur.

By using signal processing, machine learning, and other analysis, it is possible to predict the Remaining Useful Life of a system from its conditional indicators. This empowers business owners and maintenance professionals to organise maintenance activities only when the system requires it. The existence of RUL as a tool for transformational change, makes Predictive Maintenance a powerful tool for minimising downtime, eliminating unnecessary maintenance costs, and maximising customer satisfaction.

Systems that Benefit from Predictive Maintenance

The premptive nature of Predictive Maintenance can be used to save money and increase performance of many data connected systems. For example;

• Surgical Robots
• Aircraft Systems
• Industrial Robots
• Internal Combustion Engines
• Trains
• Elevators
• Batteries
• Gearing
• Pumps and Motors

In fact, any system that can expose telemetry data relating to its physical state can benefit from Predictive Maintenance in one way or another.

A PdM Example

For example, it's emperative that industrial robots track position accurately smoothly and in accordance with the target position. So, any play or backlash in joints of the robot would be suboptimal, comprising quality and may even trigger safety alarms. This is where a Predictive Maintenance strategy comes in...

Through the right application of PdM techniques, the torque and position of the robot joint can be combined to create a conditional indicator that is indicative of the play in the joint, or even the overall stiffness. When this indicator starts degrading, alerts can be put in place such that maintenance can be organised before the inevitable point of failure. Further to this, prediction of the RUL can be employed to help prioritise service activities across assets within a fleet.

The above is just one example of what predictive maintenance can do, but PdM has a wide range of applications, in almost all industries. Including but not limited to:

• Manufacturing and Industry 4.0
• Aerospace & Aviation
• Automotive and transport
• Robotics
• Energy
• Buildings and Construction
• IT Services
• Chemical industry
• MedTech

In essence, Predictive Maintenance is more than just a maintenance strategy – it's a game-changing paradigm shift that revolutionises efficiency, performance, and reliability across industry. By leveraging the power of data-driven insights and preemptive interventions, businesses can transcend the limitations of reactive maintenance, propelling their operations towards unprecedented levels of uptime, cost-savings, and customer satisfaction. Embracing Predictive Maintenance is no longer an option; it's a necessity for any organisation seeking to gain a competitive edge in today's data-driven landscape, where optimal service operations and unwavering customer satisfaction are the ultimate differentiators.