predictive maintenance & CBMHome / Case studies / Faurecia
USE CASE — AUTOMOTIVE & INDUSTRIAL PRESSES
Faurecia: predicting quality defects on presses, hours in advance
By exploiting the presses' native data, Monixo distinguishes a healthy regime from one announcing a defect — and objectively validates the effect of corrective actions.
Monitored equipment & environment
Industrial presses, hydraulic circuits, slides and cycle variables.
What the client set out to solve
Identifying regimes that signal a quality defect and verifying how effective corrective actions really are.
How the solution answers the need
Cycle-pattern recognition, multivariate indicators (hydraulic pressures, slide displacement, binary cycle states, time patterns) and ANN learning models that distinguish a healthy regime from an abnormal one, hours before the fact.
The business impact
Earlier detection of quality defects, a better understanding of press behaviour, and objective validation of corrective actions.
What comes next?
The approach can be industrialised on other presses by adapting the models to the signals available and to the quality defects specific to each line.
A similar use case to qualify?
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