Unplanned maintenance is Costly!

Unplanned downtime due to maintenance, drives production costs upwards, not only in terms of production time lost (Reduced Plant Velocity or Throughput, Low Machine Availability, High Spares Inventory Cost) but also in terms of product quality and rework (Poor Quality Yield, Rework, Increased Waste). Tightly controlled predictive maintenance can alleviate these problems.

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MVP-based

Maintenance Solution

The model has been trained to generate different types of copy for the product, including its name, a short description and a longer description that builds on top of the short description by elaborating on the different elements captured in the short description. This ensures that all product related copy on the site is generated automatically and increases the speed with which the client can refresh its online inventory.

Specific, Actionable

Insights

Our MVP-based approach to Predictive Maintenance Analysis ensures that the client receives actionable alerts that achieve the client’s ROI targets. We provided the client with Predictive Analysis, Asset-specific Maintenance Assessments, Baseline Definition and Gap Assessment using Voltage, Current and Vibration related continuous measurements.

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Deploy Infrastructure

We deploy industry standard IOT sensors and data servers to collect the required physical, or electrical measurements from target machinery

Deep-learning Model

A pretrained deep learning model, is then used to analyze the historical data, and pinpoint adverse occurrences of machine failure; the model is then used for predicting future adverse events

Immediate Business Benefits

NLP was leveraged to learn to describe the extracted product features and stitch together the feature descriptions into the product copy.

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