CASE STUDY

Sensor Analytics Reduces Machine Failures by 60% for a Manufacturing Company

Sep 23, 2016

Business Challenge

Detecting machine failure before it occurs. One of the leading manufacturing companies wanted to set up a statistical model for predictive machine failure.

Situation: Setting up predictive model for proactive machine maintenance.

Client wanted to set up a predictive model for machine failure for proactive machine maintenance. They wanted QuantZig to help define data capture methodologies, setup the model and manage it on an on-going basis.

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Solution

Fourier and SVM algorithm, time series modeling and machine learning algorithm.

We conducted an in-depth analysis of the temperature, vibration, acoustic, forces, deflections and similar technical inputs. We created a model with combination of time series modeling and machine learning algorithm. We employed advanced techniques of Fourier and SVM algorithm for data transformation, classification and predicting future events.

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Impact

Reduction in failure by up to 60%.

One of the leading manufacturing companies gained an excellent system for tracking data from the machines and predicting the failures. They also gained a proactive and preventive process for maintenance and repairs which helped them reduce overall cost due to machine failures and repairs. They were able to reduce failure events by up to 60% through on-time maintenance.

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