Automotive Industry

AutomobileIndustry

A leading automobile manufacturing industry that produces around 4 million cars per year, approached PREDICT to provide a solution that could predict failures up front, assist in efficient maintenance activity planning and, minimise the overall financial impact.

2.5 months Prognostics

Predicted the failure of linear motor 2.5 months in advance

Root Cause Identified

Deposits between the motor’s sliding boards led to the failure

16 hours of Production

Delayed maintenance resulted in 16 hours of downtime

challenge

The automobile manufacturing industry was looking for a solution that could

  • Improve the operational availability of the assets

    After a meticulous engineering study of the assets in the installation, a model was formulated to monitor the behaviour of the assets. This enabled in providing regular feedback to boost the operational capability of the assets.

  • Reduce general expenses by replacing the right part at the right time

    By using efficient  predictive analytic algorithms, the remaining useful life of the asset is estimated so that maintenance could be performed with minimum intervention to the production activity.

  • Optimise resources to reduce the preventive workload

    By predicting the assets that would break in the future and, identifying the root causes that may lead to the asset breakdown, there was a substantial decrease in the preventive workload.

solution

Analysing the engineering behind the asset and associating it with the historical data of 6 months from different sensors, PREDICT was able to model the behaviour of the system and identify the best indicators to predict the failures.

The solution when deployed in the client’s environment, was able to monitor newly installed equipment. By integrating inputs from the domain experts on the client’s side, PREDICT could further improve its solution in identifying the root causes.

Within a month of installation, PREDICT was able to predict a breakdown in one of the newly installed linear motors 2.5 months in advance. In addition, PREDICT could also determine that the deposits between the sliding boards of the linear motor would result in the failure of the linear motor.

However, maintenance was not performed on the linear motor and, the linear motor broke down within the time frame PREDICT estimated. The client could have saved 16 hours of production downtime if the maintenance was performed on time.

how can we help you?

Contact us to know how Predictive Maintenance can transform your organisation.

Imerys is fully satisfied by the performance of PREDICT and work regarding the implementation of the Kasem software within the Monicalc project, at the Imerys Minerals Site in the UK.

Imerys Minerals Limited

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