The following case study reports the methods used and the results achieved by MIPU with a project whose objective was to avoid faults through the application of Machine Learning. The project has been developed for a client company working in the manufacturing industry.


Machine Learning applications for Predictive Maintenance are used to identify the occurrence of a failure, before this happens. Those who are familiar with the P-F Curve know that the quicker you identify a potential defect, the sooner you avoid machine downtime.

  • – The first step of a Machine Learning analysis process requires the creation of an asset’s mathematical model. This model includes all the process parameters associated with that specific asset. These parameters normally are stored in a database, which acquires data from plant DCS, associated PLCs, electronic registers etc…
    For instance, if you’re designing a pump model, things as suction and discharge pressure, control valve position, bearing temperature and vibration are some good examples of parameters to include in the model. Most of the models have between 10 and 30 parameters, but there are models that have almost 100 parameters.


  • – As second step, parameters historical data are imported into the model. This dataset is generally known as “training” data set and it normally includes a year of data. One-year dataset allows the model to take into consideration the seasonal variations of management operations. An expert in asset functioning knows which data are to be included or excluded within the training set, because he/she has specific competences in this field: a strong domain knowledge.


  • – During the third step, the training dataset is used to develop an asset operational matrix. This matrix identifies how the machine should work in a precise moment, on the basis of the training data used to create it.


  • In the last step, the software constantly monitors the machine operations and predicts the values of the machine parameters according to the matrix that has received as input. If a parameter deviates from the prediction of the model with a significant percentage, the system creates an alert related to that specific parameter. Then, a technical analysis is executed on the asset in order to evaluate the change of condition and the reasons that might have caused it.
    (Can your software do it? If not, you may want to upgrade it)




Picture number 1 shows a bearing vibrational increment of a ventilator fan, caused by an oil leak. This condition generated an alarm. The solution created using Machine Learning predicted a bearing vibration of about 3,5mm, given the operating conditions. The bearing vibration slowly deviated from the predicted value, creating an alarm as soon as it reached the value of 4,7mm.
Thus, the plant technical managers were alerted and through fan visual inspection they identified an oil leak. The ventilator vacuum was actually vacuuming up the oil spilled from the leak in the fan lodging. For this reason, there was no leak sign on the ground. The oil on fan blades accumulated dirt and debris, causing a rotation imbalance and consequently a vibration increment. The plant technical managers were able to take corrective actions to stop the leak before the bearing was damaged.

Predict failures with machine learning
Increase of Fan Vibration


Picture number 2 concerns the lubrication system of a big pulverizer. The lubrication system provides oil to the gearbox and to all the bearings. The asset model predicted a temperature of 90° F, but the real temperature reached 110° F. Therefore, the software generated an alarm for the plant technicians, who discovered that the control valve of the cooling water of the heat exchanger was not functioning. The control valve was replaced and the system started working again.

Predict failures with machine learning
Pulverizer Oil Temperature


Picture number 3 is about an electro-hydraulic control (EHC) system that verifies the valve position, turbine velocity and security valves. In this case, the differential pressure through the EHC pump “A” filter began to increase. Technicians were alarmed in time and they were able to switch from pump “A” to pump“B”. In this way, it was possible to avoid the emergency shutdown of the turbine and all the connected damages.

Predict failures with machine learning
Electro-hydraulic System Filter

To know more about this case study or to learn how to create machine learning models for your assets, contact us!

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