Detecting the Wear-and-Tear of Rotor Blades Using Analytics for a Large Industrial Manufacturer

Business Objective

Our client is a leading manufacturer of industrial products. The client’s Fluids division provides an integrated solution where strands of polymer are passed to the rotor which cuts the polymer in the desired configuration. The continuous usage of the rotor makes its blade dull leading to unusable pellets of wrong dimensions. This resulted in the wastage of resources and impacted the client’s revenue.

The main objective of this engagement was to identify the optimal number of sensors and the best positions to mount them on machines in order to detect the transition of rotor blade states from sharp to dull.

Challenges

Huge volume of data from multiple sensors
Difficulty in attaining high model accuracy as sensor data is time-based and requires multiple transformations
Need for a lightweight solution as the client wanted to deploy the solution on edge devices

Solution Methodology

Collated input datasets from sensor data and rotor configuration data
Collected blade classification data at a frequency of 200Hz for 2 minutes for each Rotor configuration
Performed data cleansing and Feature Engineering to prepare a model-ready dataset
Combined data across individual sensor positions from all machines and used them to train the model
Retrained the Model using important features to make it lightweight and improve inference time for edge deployment
Created separate models for each sensor position to identify which position is ideal to detect blade wear & tear
Applied multiple independent approaches (FFT, Wavelets, PSD) to achieve high accuracy

Business Impact

Achieved a 66% reduction in data and cost of sensors
Achieved an accuracy of ~100% and were able to clearly classify blades from the identified optimal sensor positions
Deployed solution on edge by developing a lightweight solution through computationally inexpensive features without compromising on accuracy for real-time inferencing
Sensor deployed on optimal position will be used for blade classification in the future

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