This document discusses predictive maintenance through acoustic data analysis in an industrial setting. It describes using sensors and data collection from machines to detect potential issues through acoustic and vibration analysis. The customer case study involves implementing a predictive maintenance platform using acoustic data from an air compressor to identify mechanical, electrical, and other problems to decrease maintenance costs and increase machine availability. The solution involves collecting machine and sensor data, storing it in an IIoT platform, and performing analytics using predictive models and dashboards.

1. Predictive maintenance by
analyzing acoustic data in an
industrial environment
Philippe Duhem
June 2016
CONNECT ACTTHINK

2. 2© Sogeti High Tech 2© Sogeti High Tech
 Production equipment management
 Building management
 Inventory management
 Delivery tracking
 Production of customized products
Operational excellence
 Remote product upgrades
 Remote maintenance
 Data insights for engineering
Product improvements
 Pay per use models
 Lease + maintain vs. sell
New business models
IoT use cases
*Source: McKinsey
By 2025, IoT will have pervasive impact
in Manufacturing with a $2.5 trillion* impact & over
50% around operational excellence (TBC!)
IoT impact in Manufacturing for the next 10 years

3. What’s new ?
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 Sensors, PLC, Machine data
 Physical Models
 Simulation behaviors
 Empirical models, Tests data
 Scientific software
 Pre & Post treatment
 Adapting the model to real behaviors
 Thresholds, alarms
 CAX
 HPC
 LSF Family, PBS, SGE, SLURM
 Dedicated infrastructure
 Physical engineering: Structural, Thermal,
CFD, EM, Acoustics, Vibration, …
Sources
Treatment
Infra
What
 Sensors, PLC, Machine data
 Operators data
 Quality data, TRS, Maintenance Raw
material, Traceability, Tests
 Statistic analysis
 Machine Learning, Clustering, Forecast,
Decision trees
 Linear regressions, Neuronal network
 NoSQL DB, Distributed computing
framework
 Cloud
 Probability
 Predictive models
 Recommendations
MODEL DRIVEN DATA DRIVEN

4. Manufacturing Intelligence & Predictive Maintenance
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 Monitor and control production units based
on factual decisions as defined by all
collected data
 Reduce non quality costs
 Decrease Non OEE
 Master standard cycle time
 Optimize consumption (raw material,
energy…)
 Production teams will quickly identify key
factors impacting production objectives
 Physical engineering: Structural, Thermal,
CFD, EM, Acoustics, Vibration, …
Use case
Business
Values
Output
How
 Predict potential breakdowns of a machine
through data analysis
 Decrease Non OEE
 Reduce maintenance costs
 Maintenance teams will anticipate
preventive activities
 Predictive models
 Dashboard
 Recommendations
MANUFACTURING INTELLIGENCE PREDICTICE MAINTENANCE

5. Troubleshooting by data acoustic analytics
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Our Customer operates production units of energy located in France.
The objective was to decrease the maintenance costs by optimizing the maintenance
activities and machines availability rates
 Experiment acoustic and vibration troubleshooting
 Implement a global predictive maintenance platform
The target machine for the first stage is a high-powered air compressor. It represents a
strategic and critical asset for the production units
 The noise and vibration troubleshooting are used to identify mechanical, electrical,
hydraulics and aerodynamics problems. The method is based on a comparison of noise
and vibration spectra with an acoustic and vibration database
 Data storage:
 The measurement data with an operational context
 Maintenance & Machine Data
 Platform:
 Acquisition & collect: open, scalable, secure
 Analytics platform hosted in a cloud
 A rapid implementation: platform available in 1 month, models ready to use in 2 months
 Relevant statistic model supported by a model driven approach
 Scalable and secured solution based on an IIOT architecture
 Hybrid Cloud with operational treatments in the customer premises and analytics in the
CloudBenefits
Solution
Issue

6. Manufacturing Intelligence & Predictive Maintenance
6© Sogeti High Tech
Web
Mobile
DataConcentrator
Data Pipelines
Routing
Transformation
Mediation logic
Time Series
NoSQL
Relational
In Memory
Files
Search
Processing
Analysis &
Usage
Data LakeData Ingestion – IIoTData Sources - IIoT

7. IIoT Platform eObject: from edge to analytics
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IoT MODULES FUNCTIONS DESCRIPTION1. 2. 3. FEATURES4
e-OBJECT Edge Data
acquisition
• A software Agent is
embedded on : objects
(Sensors, devices, gateways)
with communication, security
and processing services.
1
Data storage &
administration
• A Middleware Platform
collects & stores of large
amount of data.
• Devices & sensors
managing services.
• Hosted in a public or
private cloud
e-OBJECT Cloud2
Data analysis
& visualization
• An Analytics Platform
analyses data from multi
sources/multi files.
• Data are transformed into
information displayed on a
visual interface.
e-OBJECT
Analytics3

8. IoT Architecture
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ActionAssignmentAnalysisAggregationAcquisition
Business
Services
Core Business
Services
Big Data
Analytics EngineData LakeM2M Layer
Connectivity
Device
Management
System
Monitoring &
Control
Orchestration
Storage
Fusion
Correlation
Analytics
Predictive
Models
Visualisation/
Dashboard
Real-time
Monitoring
Benchmark
Trends
Forecasts
+++…
Industrial
Building
Infrastructure
Predictive
Maintenance
Manufacturing
& Storage
Metering
Tracking &
positioning
Smart Grid
eObject PlatformEdge Applications
Data Sources
Security Management Cloud Management
Smart Building
ERP GMAO MES

9. Build on micro-services
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 Choose the right service for
the right use
 Interconnect to build your
application
 Use the best of each pool
Acquisition
eObject IIOT
Storage
Analytics
Data Visualization
Structuring
IoT

10. MIPM deployment phases
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DATA COLLECTION DATA STRUCTURATION MODEL & ANALYSE DEPLOY & IMPROVE
OBJECTIVES & DATA
IDENTIFICATION
 Define clear objectives
 Identify if relevant data
are available
 Prepare Change
MIPM DEPLOYMENT
 Industrial IS
 Machines connected
 Data collection
 Secure & scalable
 Data structuration
 Data Lake
 Analytics platform
 Monitoring
 Modeling
 Dashboarding
 Deployment
 Adapt, optimize
 Change management

11. Contact information
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Philippe
DUHEM
IoT & Mobility Business
Developer
philippe.duhem@sogeti.com
Tél. : +33 34 46 91 52
Mob: +33 (0)6 32 94 09 55
SOGETI High Tech
Bat. Aeropark
3 Chemin Laporte
31300 TOULOUSE

12. www.sogeti-hightech.fr
The information contained in this presentation is proprietary.
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