From Preventative To Predictive Maintenance
Preventative maintenance and planned maintenance are widely employed across many industry sectors. They are characterized by regular predetermined maintenance intervals or a component’s expected lifecycle. At these times, components are exchanged, which is why, for example, a vehicle’s cam belt is changed after it has completed a pre-determined mileage. However, component failures don’t always run to well-ordered timetables; they are random, seem unpredictable, and can be extremely expensive, as well as inconvenient when they fail without warning.
It’s better then, to keep tabs on the condition of systems and components. The ‘early warning systems’, made possible by the constant monitoring of devices and machines in operation, can help to either avoid problems before they arise or take immediate action against those that have arisen. This concept of predictive maintenance takes the pro-active principle to new heights.
If it ain’t broke, it will be
A combination of monitoring systems, sensors, and controllers all conspire to simplify processes and anticipate problems as quickly as possible. This allows for maintenance that is flexible and variable and can be shaped around the actual state of the equipment, in contrast to the rigidity of the old, tightly fixed maintenance intervals, which worked according to the timetable. In the example above, the cam belt would only be changed at the point that it needed to be, no sooner – the capacity for calamity is considerable.
So, why fix the things that aren’t broken? Replacing components at the correct time reduces waste and is much more cost effective. Predictive maintenance, therefore, applies the old adage that ‘if it ain’t broke, don’t fix it’ and adds a new one: If it isgoing to break, fix it now!
To understand when things do need fixing, statistical predictive maintenance models are created. This is done by importing ‘predictors’, which are critical values gleaned from sensor data, process measurement data, and ambient data. These values are fed into an analytics tool which recognizes certain patterns, such as the symptoms of a machine that has failed in the past.
An excellent example of pre-emptive problem solving comes from the oil and gas industry, where the failure of a drilling system can potentially cost one million dollars per hour.
In oil and gas fields, the motor temperature, motor vibration and the delivery pressure of a pump are monitored in real-time so that anomalies are immediately noticeable.
