Industrial Sensory Data Analytics

Introduction, Analysis Goals & Methods/Tools

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Chair of Technologies and Management of Digital Transformation, University of Wuppertal
Introduction
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

4
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
Pattern Recognition in Time Series Data

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Signal Forecasting
Soft Sensors & Signal Construction Interpretability and Explainability
Signal Classification & Anomaly Detection
Analysis Goals

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▪ 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.
Analysis Methods and Tools

Selected Research Projects
and Applications
Industrial Applications

and Applications
Deep Drawing of Car Body Parts

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▪ Manufacturing of car body parts with a deep
drawing tool.
▪ ~80 different parts with variable size, shape and
curvature.
in collaboration with
The Product

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Coarse cutting Deep drawingDie cutting
Water jet cutting Quality ControlCleaning
The Manufacturing Process

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Extension of the tool by a sensor system for continuous data acquisition during
manufacturing.
Using the sensory data for process monitoring and failure prediction.
The Deep Drawing Tool

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▪ Strain gauge sensors give information about the force exerted
upon the metal sheet.
▪ Course of the sensor signal indicates process failures.
The Data

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▪ Strain gauge sensors give information about the force exerted
upon the metal sheet.
▪ Course of the sensor signal indicates process failures.
The Data

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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.
The Learning Problem

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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
The Learning Model

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Results of the Signal Forecast

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From manual labor and visual inspection of the product to
automated and algorithmic data driven quality control.
In-line Signal Forecasting & Failure Prediction

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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)
Interpretability of the Model’s Decision

and Applications
Soft Sensors for Prototype Vehicles

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Determination of operating strengths and design loads for kinematic components in
production vehicles.
Extension of the car chassis of prototype vehicles with sensors to acquire load data
during operation.
Development of soft sensors for series production vehicles.
The Product, Task & Goal

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Internal control units External sensorsPreprocessing
Soft sensor models
The Soft Sensor Training Process

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Saving of electronics/hardware in series production vehicles by
replacing real sensors with AI driven soft sensor models.
Evaluating Soft Sensors in Test Drives

and Applications
Failure Classification for Railway Switches

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Remote monitoring of switching operations via current signals.
Deep learning based classification of current signals to identify faulty operations
60k switches in German railway network maintained by DB Netz AG.
Problem setting and Analysis Goal

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Database
Historical Signals of
~ 5k Switches
Classification of Characteristic Failure Offsets
Pre-processing
DTW Matching
Offset Extraction
Trimming
Input:
Offset
Signal
Convolutions
(128, 256, 128)
BatchNorm, ReLU
Global
Average
Pooling
Input:
 Operation Time
Output
(Softmax)
Failure Classification
1D-Convolutional Neural Network
FC
Layer

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Overcoming Sparse Data by Semi-Supervised Training
Reducing time to maintenance in case of operation failures!
Low accuracy of traditional ML model
Deep neural network requires many manually labeled curves (i.e. failures)
Data enrichment by using ML-Tool for decision support and labeling
Small data
catalog
Nearest neighbor
classifier
Classifier validation tool
Classification
accuracy: 84%
Classification
accuracy: 97%
1D-convolutional
neural network
Validated
training data

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

Industrial Sensory Data Analytics

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