Case Studies Aircraft component manufacturer introduces predictive maintenance

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	Aircraft component manufacturer introduces predictive maintenance

Aircraft component manufacturer introduces predictive maintenance

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics Networks & Connectivity - Gateways
Applicable Industries	Aerospace
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance
Challenge	A major European aircraft component supplier encountered this challenge first-hand. A mission-critical, programmable milling machine failed, halting the organization’s production process. Despite the customer team’s expertise, the problem proved challenging to diagnose. At first, it appeared the downtime resulted from a damaged spindle, the most complicated part of the milling machine. However, a costly and time-consuming spindle replacement did not correct the situation. The team was forced to perform an extensive system evaluation to identify the culprit. Read More
About Customer	An European aircraft component manufacturer wanted to create and implement a robust equipment monitoring and analytics solution to provide proactive, real-time insights Read More
Customer Name	Undisclosed Read More
Solution	The solution features multiple, integrated elements. By incorporating numerous XIoT devices and other monitoring applications alongside the milling machines, the maintenance solution can take a holistic view of the tools it oversees and identify the baseline characteristics for “normal” system operation. While milling machines remain within normal parameters, the system takes no action. However, should the monitoring system detect a deviation from the norm – such as changes in temperature, energy consumption, the number of spindle turns per second, or load on the spindle – its built-in analysis capability can automatically identify an appropriate course of action. Capgemini’s solution can then alert and dispatch repair personnel, or send messages to alternate staff depending on the severity of the scenario. Read More Log in to view content
Data Collected	energy consumption, Temperature Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Networks & Connectivity - Gateways

Applicable Industries

Aerospace

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

Challenge

A major European aircraft component supplier encountered this challenge first-hand. A mission-critical, programmable milling machine failed, halting the organization’s production process. Despite the customer team’s expertise, the problem proved challenging to diagnose. At first, it appeared the downtime resulted from a damaged spindle, the most complicated part of the milling machine. However, a costly and time-consuming spindle replacement did not correct the situation. The team was forced to perform an extensive system evaluation to identify the culprit.

About Customer

An European aircraft component manufacturer wanted to create and implement a robust equipment monitoring and analytics solution to provide proactive, real-time insights

Customer Name

Undisclosed

Solution

The solution features multiple, integrated elements. By incorporating numerous XIoT devices and other monitoring applications alongside the milling machines, the maintenance solution can take a holistic view of the tools it oversees and identify the baseline characteristics for “normal” system operation. While milling machines remain within normal parameters, the system takes no action. However, should the monitoring system detect a deviation from the norm – such as changes in temperature, energy consumption, the number of spindle turns per second, or load on the spindle – its built-in analysis capability can automatically identify an appropriate course of action. Capgemini’s solution can then alert and dispatch repair personnel, or send messages to alternate staff depending on the severity of the scenario.

Data Collected

energy consumption, Temperature

Impact #1	[Efficiency Improvement - OEE] Intelligent insights to maximize equipment life cycle and performance
Impact #2	[Efficiency Improvement - Asset Utilization] Significantly reduced risk of equipment downtime
Impact #3	[Process Optimization - Real Time Alerts] Automated alerting should mission-critical components require immediate attention

Impact #1

[Efficiency Improvement - OEE]

Intelligent insights to maximize equipment life cycle and performance

Impact #2

[Efficiency Improvement - Asset Utilization]

Significantly reduced risk of equipment downtime

Impact #3

[Process Optimization - Real Time Alerts]

Automated alerting should mission-critical components require immediate attention

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Overview

Aircraft component manufacturer introduces predictive maintenance

Operational Impact