Industrial Sensory Data Analysis: a research area of the chair for technologies and management of digital transformation from the university of Wuppertal, Germany. For more information, see here: https://www.tmdt.uni-wuppertal.de/de

1. Industrial Sensory Data Analysis

2. Introduction, Analysis Goals & Methods/Tools

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Introduction
Industrial Sensory Data Analysis
Pattern Recognition in Time Series Data
 The field of Natural Language Processing (NLP) has seen a number of deep learning based
advances.
 These examples include speech recognition, voice recognition and speaker separation, and are
based on recognizing patterns in the time series data that characterizes sound.
Speaker Analog signal
Analog to digital
conversion
Pattern
recognition
Application

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Introduction
 The field of Natural Language Processing (NLP) has seen a number of deep learning based
advances.
 These examples include speech recognition, voice recognition and speaker separation, and are
based on recognizing patterns in the time series data that characterizes sound.
Tool Analog signal
Analog to digital
conversion
Pattern
recognition
Quality
Industrial Sensory Data Analysis
Pattern Recognition in Time Series Data

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Signal Forecasting
Soft Sensors & Signal Construction Interpretability and Explainability
Signal Classification & Anomaly Detection
Industrial Sensory Data Analysis
Analysis Goals

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
 Supervised Machine Learning for Classification and Anomaly Detection.
 Unsupervised Machine Learning and Dynamic Time Warping for Similarity Analysis.
 Deep Learning and Long-Short-Term Memory Networks for Time Series Forecasting.
 Web Based Development Tools for Interactive Visualization Dashboards.
Industrial Sensory Data Analysis
Analysis Methods and Tools

7. Selected Research Projects
and Applications
Industrial Applications

8. Selected Research Projects
and Applications
Industrial Applications
Deep Drawing of Car Body Parts

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
 Manufacturing of car body parts with a deep
drawing tool.
 ~80 different parts with variable size, shape and
curvature.
in collaboration with
Deep Drawing of Car Body Parts
The Product

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Coarse cutting Deep drawingDie cutting
Water jet cutting Quality ControlCleaning
Deep Drawing of Car Body Parts
The Manufacturing Process

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Extension of the tool by a sensor system for continuous data acquisition during
manufacturing.
Using the sensory data for process monitoring and failure prediction.
Deep Drawing of Car Body Parts
The Deep Drawing Tool

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
 Strain gauge sensors give information about the force exerted
upon the metal sheet.
 Course of the sensor signal indicates process failures.
Deep Drawing of Car Body Parts
The Data

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
 Strain gauge sensors give information about the force exerted
upon the metal sheet.
 Course of the sensor signal indicates process failures.
Deep Drawing of Car Body Parts
The Data

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Network Input Prediction
Target
Regular Production Cracking Metal Sheet
 Forecast of the strain gauge signal based on a limited cutout.
 Extraction of relevant information to predict the course of the sensor signal.
Deep Drawing of Car Body Parts
The Learning Problem

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Strain gauge
cutout
Strain gauge
forecast
30 30 30 2
Classifier
LSTM
Input
LSTM
LSTM
128 128 128 128
bi-LSTM
Input
bi-LSTM
bi-LSTM
bi-LSTM
Output
Regressor
Deep Drawing of Car Body Parts
The Learning Model

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Deep Drawing of Car Body Parts
Results of the Signal Forecast

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
From manual labor and visual inspection of the product to
automated and algorithmic data driven quality control.
Deep Drawing of Car Body Parts
In-line Signal Forecasting & Failure Prediction

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
What are the important parts of the signal that lead to the prediction of a process failure?
Baseline (trained on the complete signal) Prediction case (trained on the cutout signal)
Deep Drawing of Car Body Parts
Interpretability of the Model’s Decision

19. Selected Research Projects
and Applications
Industrial Applications
Soft Sensors for Prototype Vehicles

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Determination of operating strengths and design loads for kinematic components in
production vehicles.
in collaboration with
Extension of the car chassis of prototype vehicles with sensors to acquire load data
during operation.
Development of soft sensors for series production vehicles.
Soft Sensors for Prototype Vehicles
The Product, Task & Goal

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Internal control units External sensorsPreprocessing
Soft sensor models
Soft Sensors for Prototype Vehicles
The Soft Sensor Training Process

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Saving of electronics/hardware in series production vehicles by
replacing real sensors with AI driven soft sensor models.
Soft Sensors for Prototype Vehicles
Evaluating Soft Sensors in Test Drives

23. Selected Research Projects
and Applications
Industrial Applications
Failure Analysis for Railway Switches

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
in collaboration with
Remote monitoring of switching operations via current signals.
Similarity analysis of current signals to identify faulty operations and failure categories.
60k switches in German railway network maintained by DB Netz AG.
Remote Sensor Diagnosis for Railway Switches
Problem setting and Analysis Goal

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Database
> 1M signals
Pre-processing
Trimming, Extraction
Euclidean Matching
DTW Matching
Similarity Computation
Dynamic Time Warping
Failure Categorization
Hierarchical Clustering
Remote Sensor Diagnosis for Railway Switches
Similarity Analysis and Clustering of Time Series Data

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Industrial Sensory Data Analysis
Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Regular switching operations Faulty switching operations Type 1 Faulty switching operations Type 2
Reducing time to maintenance in case of operation failures and
enabling future predictive maintenance scenarios.
Remote Sensor Diagnosis for Railway Switches
Categorization of Failure

27. Richard Meyes, M.Sc.
Tel: +49 (0)202 439 1046
meyes@uni-wuppertal.de
Chair for Technologies and Management of Digital Transformation
Univ. Prof. Dr. Ing. Tobias Meisen
https://www.tmdt.uni-wuppertal.de/
Campus Freudenberg
Rainer-Gruenter-Str. 21
D-42119 Wuppertal
Germany
University of Wuppertal
School of Electrical, Information and Media Engineering