industrial big data analytics for quality improvement in ... · online monitoring of big data...

Lab for System Informatics and Data Analytics (SIDA)

Industrial Big Data Analytics for Quality

Improvement in Complex Systems

Department of Industrial and Systems Engineering

University of Wisconsin-Madison

Dr. Kaibo Liu

1


Background

• A.P. 2013-now, Department of industrial and Systems Engineering, UW-Madison

• Ph.D. 2013, Industrial Engineering (Minor: Machine Learning), Georgia Institute of Technology

• M.S. 2011, Statistics, Georgia Institute of Technology

• B.S. 2009, Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Hong Kong

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My Research & Expertise

Research Interests Expertise

System Informatics and data analytics:

• Complex system modeling and performance assessment

• Data fusion for online process monitoring, diagnosis and prognostics

• Statistical learning, data mining, and decision making

Multi-disciplinary Research

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Spatiotemporal Field Modeling and Prediction

Sensor Measurement and Monitoring Strategy

System Degradation Analysis and Prognostics

Engineering

Statistics/ Machine Learning

Operation Research/

Control

Multidisciplinary approach

Overall, my research goal is to make sense of big data for better decision making!

Lab for System Informatics and Data Analytics (SIDA) 4

Sensor Measurement and

Monitoring Strategy


Objective-oriented sensor system designs in

complex systemsObjective• Obtain an optimal sensor allocation design at

minimum cost under different user specified quality requirements

Results Summary• Ensure customer satisfaction by optimally

designing sensor allocation strategy• The average cycle time, cost and inventory

level can be greatly reduced• Algorithms have been tested in several

applications, e.g., the hot forming and the cap alignment processes

• Supported several studentsEffectively search for optimal sensor system design solutions

Approaches• A best allocation subsets by intelligent search,

named BASIS algorithm that intelligently searches for the optimal sensor allocation solution

• Features• Consider the trade-off of detection speed,

fault diagnosis accuracy, and cost savings


Causation-based monitoring, diagnosis and

controlObjective• Transform from existing correlation-based

techniques into a new causation-based quality control paradigm to achieve effective online quality monitoring and inference, root cause diagnosis, and proactive process control

Approaches

• Features• Engineering knowledge enhanced causal

modeling• Causation-based online quality monitoring,

inference, and diagnosis• Causation-based online feed-forward and

feed-back process control

Results Summary• Establish a series of causation-based

monitoring, diagnosis and control techniques for quality improvement in complex systems

• Algorithms have been tested in the hot forming, the cap alignment, and the rolling processes

• Supported several studentsimproved efficiency, yield, and quality


Online monitoring of Big Data Streams

Objective• Create a new paradigm of dynamic data-driven

modeling, sampling and monitoring schemes for Big Data Streams (e.g., Video streams)

Approaches• A self-updated statistical model to fully

characterize the changing background• A dynamic, data-driven sampling strategy

subject to practical resources constraints • A scalable and robust statistical process

control method tailored for Big Data Streams

• Features• Scalability: linear complexity that ensures

practical implementation• Adaptability: automatically localize the

anomaly regions without any prior knowledge

Results Summary• Establish a series of real-time monitoring

methodologies that are tailored for Big data streams for quick anomaly detection (either cyber of physical) and localization

• Algorithms have been tested in various applications, e.g., diaper manufacturing, climate monitoring and solar flare detection

• Supported several students

Examples of thermal profiles on the polishing pad

during CMP process under different conditions

Maximize the detection capability with practical resources constraints


Dynamic Data-Driven Modeling, Sampling and

Monitoring for Real-Time Solar Flare Detection

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(a) Applications𝑡

Original Solar Image

(b) Applications modeling

Updated Solar Image

(c) Application measurement

systems and methods

Dynamic Sampling

𝑡

DDDAS

Framework

(d) Mathematical and

statistical algorithms

SPC Chart

Update

Model

Update

SPC

Update samplingSample data

• A dynamically updated

spatial-temporal

statistical model fully

characterize the

changing background

• A dynamic sampling

algorithm that

actively decides

which data streams to

observe given the

resources constraints

• A scalable and robust

SPC to effectively

combine the information

from significant data

streams to produce an

overall global

monitoring system


Sensor Measurement and Monitoring Strategy

• Objective-Oriented Optimal Sensor Allocation Strategy: determine the minimum number of sensors needed given user specified requirements

• Adaptive Sensor Allocation Strategy: Adaptively adjust sensor allocation in a Bayesian Network to enhance monitoring and diagnosis

• A Top-r based Adaptive Sampling Strategy: Online monitor normally distributed big data streams in the context of limited resources

• A Nonparametric Adaptive Sampling Strategy: Online monitor non-normal big data streams in the context of limited resources

• Effective Online Data Monitoring and Saving Strategy: intelligently select and record the most informative extreme values in the simulation data

• A Spatial Adaptive Sampling Procedure: leverage the spatial information and adaptively and intelligently integrate two seemingly contradictory ideas (Wide and deep searches)

• A Rank-based Sampling Algorithm by Data Augmentation: automatically augment information for unobservable variables based on the online observations


System Degradation Modeling and

Prognostics


Internet of Things-enabled Condition-based

Monitoring, Diagnosis, and Prognostics

Objective• Leverage condition monitoring signals

collected from multiple and heterogeneous sensors to better visualize and assess the current system health status and predict its future behavior in real time

Approaches• Novel data fusion methods that select

best sensors and combine their information to construct health indices for system performance assessment

and visualization, ℎ𝑖,𝑡 = 𝑓 𝒙𝑖,.,𝑡

• Features• Combine data-driven approaches and

engineering principles governing the underlying failure mechanism to ensure satisfactory performance

Results Summary• Establish a series of data fusion

methodologies that are tailored for IoT-enabled service systems for health status visualization, characterization and prediction

• Algorithms have been tested in various applications, e.g., engine health monitoring, Alzheimer's disease and forklift management


Aircraft engine diagram

Better health status characterization

Better fault diagnosis

Better RUL prediction


Case Study – Engine RUL prediction

Name T24 T50 P30 Nf Ps30 phi NRf BPR htBleed W31 W32

Value 0.13 0.37 -0.03 -0.05 0.23 -0.21 -0.08 0.16 0.12 -0.05 -0.16

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• Optimal weights 𝒘∗: ℎ𝑖 𝑡 = 𝑳𝑖 𝑡 𝒘∗

T24…

Health index

W32

The stochastic degradation models

(Gebraeel, 2006)Bayesian updating methodsReal time sensor

information

Remaining life prediction

• Developed HI-QL improved the RUL prediction accuracy

o by 64.83% compared with the best single sensor

o by 20.7% compared with existingHI-based models


System Degradation Modeling and Prognostics

• Non-parametric data fusion model: does not need to know the parametric form of the degradation signal

• semi-parametric data fusion model: integrate degradation modeling and prognostics in an integrated manner

• SNR-based data fusion model: immune to the heterogeneous sensor challenges in terms of signal scales and measurement units

• Quantile regression-based data fusion model: ensure to recover the underlying degradation status with estimated fusion coefficients converging to the true values

• Sensory-based Failure Threshold Estimation: online update the failure threshold estimation of the in-field unit

• Kernel-trick for nonlinear data fusion model

• Generic data fusion model with automatic sensor selection

• Data fusion model for multiple failure modes

• Data fusion model when there are multiple environmental conditions

• Generic data fusion model when mutisensor signals are asynchronous

• Dynamic control of degradation speed and RLD via workload adjustment


Smart Monitoring of Alzheimer’s Disease via Data Fusion,

Personalized Prognostics, and Selective Sensing

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The model of AD trajectory [3]

Existing Screening Approaches

New Methodology

Biomarkers Screening Tests Smart Monitoring

Effective-ness

Expensive, e.g., $ 5000 per scan for

PiB-PET

Passive information collection:

burden, and complexity

Proactive information

collection driven by accurate

statistical models Proposed Smart Monitoring Method


Data-Driven Failure Predictive Analytics for

Internet of Things (IoT) enabled Service Systems

Establish a core set of data-driven modeling, failure prognosis, and service decision-making methodologies for emerging Internet of Things (IoT)

enabled service systems, particularly in the context of TMHNA

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Historical off-line dataon multiple units

Time0

Condition monitoring (CM) data

Failure

Censored

Time-to-failure data

Fai

lure

cas

es

Failure event data

Real-time on-line CM dataon individual units

0 5 10 15

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Time

CM

Sig

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Car #1 signal

Car #2 signal

Car #i signal… …

.

Equipment

in the field

Communication

network

Back-office

Processing center

Sensing dataService alert

Unit

Unit

Unit


Big data analytics solutions to improve nuclear power

plant efficiency: Online monitoring, visualization,

prognosis, and maintenance decision making

Advance the ability to assess equipment condition and predict the remaining useful life (RUL) to support optimal maintenance decision

making in nuclear power plants.

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Spatiotemporal Field Modeling

and Prediction


Real-time travel demand modeling and

prediction in smart and connected citiesObjective• Online prediction of the origin-destination

(OD) demand in traffic networks • Existing literature models the demand count

data separately for different OD pairs without considering spatial correlations or domain knowledge

Approaches• Propose a multivariate Poisson log-normal

model with specific parametrization tailored to the traffic demand problem

• Capture the spatiotemporal correlations of the traffic demand across different routes and epochs and automatically clusters the routes based on the demand correlations

• The model is estimated using an Expectation-Maximization (EM) algorithm and applied for predicting future demand counts at the subsequent epochs

Results Summary• The proposed method integrates traffic

network domain knowledge and achieves a sparse estimation based on clusters of routes.

• Estimate the parameters of the model accurately with the developed EM algorithm

• Has been applied on a real New York yellow taxi dataset


ഥ 𝝁

𝑡


Modeling of dynamic thermal fields via

grid-based sensor networksObjective• Accurate modeling and estimation of the full-

scale grain thermal field based on the grid-based sensor networks.

• Challenges:• Grid-based but sparse sensor data• Spatiotemporal correlation structures• Local variability of grain temperature

Approaches• Integrate physical dynamics model (for global

profile) and spatiotemporal stochastic processes (for local profile)

• Develop a spatiotemporal transfer learning technique for 3D field estimation using sensor observations from several homogeneous data sources

• Estimate time-varying parameters in PDE models from the obtained data to acquire a more accurate description of the dynamics

Results Summary• The proposed methods integrate physical

dynamics model, spatiotemporal statistical model, and advanced machine learning technique to achieves an accurate estimation of the 3D thermal fields based on grid-based sensor networks.

• Has been tested and verified on several real datasets for grain storage application

𝑡1 𝑡2 𝑡𝑀…

…

Time

𝑌(𝑠, 𝑡1) 𝑌(𝑠, 𝑡2) 𝑌(𝑠, 𝑡𝑀)…


Other Research Projects


Operator activity index development and

performance improvement

Objective• Propose a generic approach to develop an

effective composite index to identify high-performing operators on multiple dimensions

Results Summary• Developed an OAI by combining worker

metrics information to measure the activity of operators

• OAI by NPCA meaningfully explains the operator activity and also provides guidance for performance improvement

• Algorithms have been tested in the forklift operator activity analyses


Approaches• a new nonnegative principal component

analysis (NPCA) approach with optimal balance• Best separation of operators• Comply with practical interpretation


Obstructive Sleep Apnea Detection

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Retail Site Location Analysis by Business Data

AnalyticsObjective• Choose an optimal location for the opening of

a new retail site

Results Summary• Established a generic guideline on leveraging

data analytics tools for resolving business issues when dealing with business big data

• Algorithms have been tested in a real case study involving choosing an optimal location for the opening of a new retail site


Approaches• Estimate the new market shares of the

company over the country if the new retail site is tentatively opened at different potential locations

The company of interest conducts gas station equipment repair and replacement business, who provided a dataset contains a total of more than 1 million detailed business transactions with a size about 8 GB over the past 5 years.


Research Summary

Engineering

Statistics OR/Control

Engineering

Statistics/ Data

Mining

Operation Research/

Control

Industrial Big Data Analytics

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Acknowledgement


Thank you!

Questions?

[email protected]

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industrial big data analytics for quality improvement in ... · online monitoring of big data...

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