5136-DNP-CPCI SST

Data Requirements

PdM applies statistical methods to historical data to learn patterns and outcomes that inform the predictive algorithms once operational data is received. There are three characteristics required of the training data:

It must be relevant to the real operating state of the assets you wish to monitor
The quantity must be sufficient to allow accurate predictive models to be formed
The supplied data must have the required quality, i.e., it must have been cleaned and sorted

What this means is that while data is necessary for PdM, it must be of the right quality and quantity to be useful for any meaningful pattern recognition.

PdM is not a maintenance program in its own right but an asset strategy that forms part of a larger program. In a balanced maintenance program, you can expect individual assets to be operating under maintenance strategies applicable to each asset’s criticality, capital cost, and failure rates.

Some assets will be run-to-failure, others will receive preventative or condition-based maintenance, and a small number will be on a predictive program. Normally, the decision to implement predictive maintenance on a particular asset will be guided by the outcome of a failure mode and criticality effects analysis (FMECA).

In some cases, it is necessary to perform nonlinear scaling on a set of linear output values. There are many reasons for this type of nonlinear scaling. Consider the volume of liquid in a conical tank instead of a cylinder. The volume of a conical tank based on height is expressed as an exponential equation.

If we use the previous example of a tank containing liquid but change one of the parameters, we can better understand the nonlinear scaling scene. Let’s assume the same variable for the scenario

a laser distance sensor used to measure the liquid level in the tank. When the water tank is empty, the sensor will send an output of 10mA according to the previous example. When the water tank is full, the sensor sends a 4mA output. The difference between the linear example and this nonlinear example lies in the shape of the water tank. In other words, the height of the water tank is the same, but the width varies with the height. We will assume that the water tank is conical, with a radius at the top greater than the bottom of the tank