MSET2 Overview: “Anomaly Detection and Prediction” Oracle ... · US Marine Base Steam Plant,...

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MSET2 Overview: “Anomaly Detection and Prediction”Oracle Cloud Autonomous Prognostics

Kenny Gross, OracleLabs

Aug 8, 2019


Advanced Statistical Machine Learning for IoT Prognostic Applications

The Multivariate State Estimation Technique (MSET) is a nonlinear, nonparametric machine learning method that was originally developed at the USDOE’s Argonne Natn’l Laboratory in the 1990's for prognostic anomaly detection in nuclear plants, Nasa, commercial aviation, and business-critical industrial applications. Oracle was the first company to pull MSET-type statistical ML into enterprise servers, engineered systems, and DB clusters.

This presentation gives an overview of Oracle’s MSET2 and how it attains high sensitivity for detecting subtle anomalies in noisy or even chaotic time series metrics, but with ultra-low false-alarm and missed-alarm probabilities, making MSET2 an ideal candidate ML algorithm for dense-sensor IoT applications.


To Customers

– Downtime very costly

– Measured by lost sales, reduced productivity, damaged business reputation, diminished customer loyalty

To Oracle:

– Means of differentiation

– Goal to provide continuous application access with predictable performance

In today’s internet-based computing

model …

Mission

director,

Apollo 13

Why is High Availability Important?Motivation behind Oracle’s 19 years of development of advanced

real-time prognostic machine-learning algorithms


Original MSETdemonstrated high

sensitivity, and avoidance of false

alarms, for a variety of safety-

critical and business-critical

applications (under DOE Tech-

transfer initiatives).

NASA Space

Shuttle

US Marine Base

Steam Plant,

Continuous

predictive fault

monitoring

Online health

monitoring of

heavy earth-

moving

equipment

Health Care

Genetics

Cardiac Signals

Continuous safety

monitoring of amusement park

structuresPredictive fault monitoring in

Nuclear Plants.

Online surveillance of nuclear

safeguards sites.

Onboard Automotive Applications

Predictive fault monitoring in petrochemical

plants.

Predictive fault monitoring in manufacturing

plants.

Six-sigma applications..

Ultrasensitive environmental monitoring for

hazardous airborn

contaminants.

Improved semiconductor manufacturing

(Ongoing $18M NIST project)

Naval Applications

Condition-based maintenance.

Intelligent Destroyer Initiative

MSET Background

Original MSET (1998) is

mature and in use for

prognostics in many safety-

critical and business-

critical industries.

Oracle MSET2 inherits and

improves the value

proposition for real-time

prognostics for IoT optimal

predictive maintenance of

critical assets.

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. | 5

MSET1: DoE Funded ResearchInitially for surveillance of instrumentation in commercial nuclear plants and NASA aerospace applications

Approved by US NRC in Feb 2000

Now in all 96 US Nuclear Reactors, most of the 450 Commercial Reactors world-wide

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |Copyright © 2018, Oracle and/or its

affiliates. All rights reserved.

Delta, Southwest, Air France, Lufthansa ….

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |Copyright © 2018, Oracle and/or its


Disney Theme ParksNASA


Oracle Advanced Prognostics (MSET-2) Utility Use Cases

• Smart Meter and Grid Operations

• Energy Efficiency and Demand Response

• Strategic Asset Management & Capital Planning

• Underground (UG) primary voltage cables

• UG cable components (e.g. 200A elbows & 600A T-bodies, etc.)

• UG GIL, in-line Splices & Terminations

• OH Connectors & Splices

• Wind Turbines

• Batteries

• Load Shape Forecasting / AMI Data

• Switch Gear

• Breakers/Recolsures

• Reactive Load Forecasting

• Transformer Load Management

• Transformer RUL

• High Speed Relays

• Substation & SCADA Monitoring

• Conservation Voltage Regulation

• Customer Experience

• Cyber Security

• Supply Chain Counterfeit Electronics

• Solar Panels

Copyright © 2018, Oracle and/or its



Value-Add for Utility IoT Applications (Separate OracleLabs Whitepapers on each)

9

• Remaining Useful Life Estimation for Critical Assets

• High-Accuracy Loadshape Forecasting for Energy Industries

• Optimal Resource Allocation for Minimizing Regional Power Outages from Storm

Events

• Provenance Certification for Large-Scale Time Series Databases

• EMI Fingerprints for Passive Detection of Counterfeit Components in Electronic

Systems ($200B/yr problem in data centers and all industries Oracle serves)


Value-Add for Manufacturing Prognostic Applications (Separate OracleLabs Whitepapers on each)

10

• Higher mfg plant availability for critical assets

• Higher yield, shorter cycle times, higher throughput

• Lower scrap rates (direct cost on bottom line)

• Lower "Early-Life-Failures" (ELFs) for Manufactured Products

• Condition-based-maintenance (CBM) for mfg plant assets (saves substantial

operations&maintenance (O&M) costs compared with presently practiced time-based maintenance

strategies)

• The proven ability to achieve "smart recalls" (but which we for enterprise computing manufacturing

applications called "Surgical Recalls", which saved Sun & Oracle megabucks compared with

general world-wide recalls).


Advanced Prognostics Architecturefor High Value Assets in Manufacturing Industries

Object Storage

MSET-2

API Management

and Governance

Layer

APIs for • Real Time Alarms• RUL Estimations• other MSET-2

Capabilities

User Interfaces Layer

Intelligent Analytics Tool

Customized Application

Mobile / Chatbots

Event Hub MSET-2 Services Container

Pre-Processing

Real-Time Telemetry Feeds

Post-Processing

Model Construction and Real-Time Learning

Compute Real-Time Telemetry

Continuous Machine Learning

AutomatedFramework

Telemetry Data(from Sensors Farm)

S1

S2

Sn

S4


12

MSET & SPRT

MSET&

SPRT

2002 Sun Microsystems licensed MSET IP Rights; Sun Microsystems begins innovations

2000 USNRC to allow use of MSET for all US nuke plants;

2002 2017

Oracle: 18 years and 50+ innovations around MSET & SPRT

2005

2011 GE Digital acquires SmartSignal for $220M- MSET1 becomes Foundation for GE_Predix- MSET1 vulnerable to signal/sensor issues not

addressable by conventional ML…all of whichOracle MSET2 solves…see below and subsequentslides)

2011 2014

2009 Oracle Acquires Sun Microsystems; Oracle continues innovations

SmartSignal Commercializes MSET & SPRT in Transportation, Nuclear Plants, DoD, Manufacturing, Locomotives, NASA, etc.

2016 MSET1 PatentsExpire

2000 Principal MSET Inventor/Founder joins Sun Microsystems

1989 -- 1999 DoE funded research, Multiple Universities, EPRI, Babcock & Wilcox, Utilities, DoE&DoD National Laboratories

1989

1998 Original Patent MSET, SPRT Issued; IP transferred to SmartSignal Inc.

Oracle Cloud Advanced

Prognostics

MSET-1

MSET-2


Oracle’s MSET2 Oracle, over the last 18 yrs has made extensive use of MSET for business-critical assets in data centers, and has developed a dense portfolio of over 4 dozen Oracle patented innovations that leverage MSET2 (as a core algorithm) but integrated with various pre-processing, post-processing, and optimal training/tuning algorithms so that Oracle's prognostic solutions are more robust to low resolution sensors, data acquisition limitations, missing values in time series signatures, intermittent spurious anomalies, signal-asynchrony issues in large-scale IoT applications, etc. MSET2 attains higher sensitivity and better false alarm avoidance than any alternative Machine Learning approaches, including neural nets, support vector machines, and kernel regression.

–13

.


Background: Prognostic Algorithmic Innovations

Sequential Probability Ratio Test (SPRT)

Advanced pattern recognition technique for high sensitivity, high reliability sensor and equipment operability surveillance.

Developers proved in refereed journals that the SPRT provides the earliest mathematically possible annunciation of a subtle fault in noisy process variables.

Crucial capability for IoT critical-asset health monitoring: Ultra-low and separately specifiable false-alarm and missed-alarm probabilities (Type-I and Type-II errors)

Multivariate State Estimation Technique (MSET)

Online model-based fault detection and identification.

MSET predicts in real time what each process metric should be on the basis of learned correlations among all process variables.

MSET incorporates the SPRT to monitor the residuals between the actual observations and the estimates MSET predicts on the basis of the correlated variables.

For Stationary

Time Series

For Dynamic

Time Series


1) Online model is “learned” from operating telemetry data

2) Online model provides an estimate for each new observation value

3) MSET alarms when estimated and observed data disagree

How MSET is AppliedTraining

Data

Calibrate

Model

Online

Model

Acquire

Data

Parameter

Estimation

Fault

Detection

Fault

Found

?

Alarm or

Control

Action

Asset

Yes

No

Training

Monitoring


OracleLabs Prognostics Innovations for IoT ApplicationsTypes of Sensors MSET2 Prognostics Algorithms Work Well With:

16

Electrical (current, voltage, power)

All types of thermal transducers, FBG optical thermometry

Well logging, bore-hole logging instrumentation including gamma, neutron instrumentation

All physical transducers used in drilling, SCADA, and refineries

Pixelated infrared 2D thermography (where available)

Tri-axis accelerometers

Tachometers, proximity-transducers for rotating shafts, (for any/all assets involving rotating machinery...pumps,

turbines, blowers, fans, motor/generators, etc)

All types of fluid flow sensors, including venturi-flow sensors and electrohydrodynamic (EHD) flow sensors for

conductive fluids

HFCT (High Frequency Current Transformers)

Ambient environmental sensors: Pressure, Relative Humidity, Anemometry

Time-Domain Reflectrometry (TDR) (if used for signal and interconnect integrity validation)

UHF (Ultra High Frequency) Sensors

Acoustic sensors (we have Oracle patents on high-accuracy incipient fault prognostics in mechanical and

electromechanical systems from inexpensive acoustic sensors processed with our proprietary algorithmics)

FMC (Flexible Magnetic Coupler) Sensors


Traditional Threshold-Based Surveillance Inadequate for IoT Prognostic Applications

Traditional threshold-based prognostic approaches may use Machine Learning to distill down and coalesce important metrics for distinguishing between “normal” and “anomalous” behavior, but ultimately metrics are being compared against a threshold:

The endemic problem with threshold limit tests is the “sea saw” effect between false alarms and missed alarms.

If the user wants to get earlier warnings for developing problems and “squeezes” the thresholds closer to the means, we get spurious trips and high false alarm rates.

If the user wants to avoid costly false alarms and moves the thresholds further away from the distribution means, then the assets can be severely degraded (or failed/crashed) before any alerts are generated.


Traditional Condition Monitoring

Monitors all signals separately

SPRT Alarms

Sensor 4

By creating a dynamic band around each sensor value in real time and correlating it to other sensor values, MSET-2 is able to give an Early Warning

MSET-2 Monitorsand correlates all sensors

simultaneously

Threshold Trip

Early WarningTime Difference

Early Warning AdvantageEarly Warning

Could be days, weeks, or months before traditional

Threshold trip warning

Threshold Trip

Upper

Lower

8/8/2019


ACTUAL PARAMETERS (YELLOW) VS.

MSET PREDICTED (RED):MSET2: Unprecedented

Prognostic Sensitivity

MSET2 detects incipient degradation that is still "within the noise band” (Impossible for conventional threshold based surveillance)

Note very subtle disturbance is introduced into a system parameter starting at DAY=0.

SPRT alarms start triggering at DAY=13, when the degradation is only 0.06% of the signal, and well within the noise band.

[This degradation mode was not detectable by the Utility’s diagnostic monitoring tools until day 56 – Seven weeks after MSET2]

Degradation detected by SPRT:

Residuals monitored by SPRT:


Legacy Viewgraph from an operating nuclear plant: MSET detects instrumentation

degradation that threshold-limits cannot detect

Departure between real signal (yellow) and MSET estimate

(red) at onset of instrument degradation event.

Onset of SPRT alarms for Instrument R241


Intellengnt Vehicle and Highway Systems (IVHS)

and MSET

Early DOT-sponsored research into autonomous automobile technology

Multiple sensor inputs: Tach, acceleration, speed, GPS, gyroscope, compass, wheel differential sensors

Challenge with Early Prototypes:

Compass signal occasionally subject to interference (e.g. metal bridge, local EMF)

Solution:

MSET integrated with Intelligent Vehicle algorithm. When MSET catches compass disturbance, it dynamically sets weighting factor to 0 in the vehicle localization algorithm.


Sensor “Loss-of-Gain” failures lead to costly outages in Utility, Oil&Gas, Avionics, and other industrial IoT applications, and to loss of lives in safety-critical applications.

Thresholds cannot catch this sensor degradation mode.

MSET2 detects this degradation mode with very high accuracy, no false alarms.


MSET2 Spinoff: EMI FingerprintsFor Passive Detection ofCounterfeit Electronic Components($200B/yr problem across all Govt andCivilian electronic system use cases)

Oracle Confidential8/8/2019


Oracle IP Portfolio: MSET2-Based “EMI Fingerprints” for Enhanced Prognostics of Enterprise Computer Systems, and AntiCounterfeiting of Electronic Components

EMI Telemetry coupled with Adv Pattern Recognition (MSET2)

EMI wireless telemetry signatures detected with an incredibly cheap sensor (half-inch wire antenna)

Enhanced “Electronic Prognostics” (proactive detection of incipient degradation in solid-state components, subsystems)

Automated detection of counterfeit components, a $200B per yr problem in the electronics industry


Inexpensive “Sensor” Used for EMI Telemetry

Short segment of stripped wire:


Typical EMI Frequency Spectrum: Frequency range divided into “bins”. For each discrete bin, the observations trace out a time series signature. When dynamic loads are running on the server, the EMI time series are well correlated with conventional physical telemetry signals (e.g. Temperature, Voltages, Currents).

Frequency

Range is

Binned


EMI Fingerprint: OracleLabs’ Patented analytical technique identifies the major “ridges” and computes correlation patterns.


Counterfeit Electronics

Counterfeit electronics has become a $200B per year problem across all electronics industries.

2017: US NIST says that the international distribution of counterfeit electronic components is 900% more profitable than the international distribution of cocaine…. But harder to detect.

Dogs can sniff cocaine at supply-chain checkpoints, but not counterfeit electronics.

OracleLabs EMI Fingerprint Technology provides the passive means to “sniff out” counterfeit electronics.


Proposed MagMount Software-Defined-

Radios (SDRs) with Wireless Beacon

Antennas for Counterfeit Detection in Utility

Assets


Golden System

Frequency range divided into “bins”. For each discrete bin, the RF observations trace out a time series

signature

Copyright © 2018, Oracle and/or its


Counterfeit Components


ARPAnalytical Resampling Process*


* OracleLabs Issued Patents 7,292,659 7,391,835 8,214,682 8,365,003 7,573,952

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |10/27/15

•ARP: Essential for Multi-Signal Diagnostics/Prognostics•Challenges for multi-sensor diagnostics/prognostics, plus ARP solutions to those challenges, summarized next slide

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal33

ARP: Oracle Competitive Differentiation:

OracleLabs’ ARP Innovations Assure Optimal Machine Learning

Prognostics for All Types of Variable Sampling Rate, Variable

Clock-Skew Challenges across all IoT Industries

"Correlating and Aligning Telemetry Signals for Computer System Performance Parameters," K. C. Gross, V.

Bhardwaj, D. M. Fishman and L. Votta, Case ID Oracle-P8596 U.S. Patent 7,292,659 (Nov 6, 2007).

"Genetic Algorithm Approach for Optimal Phase Shift Synchronization of Telemetry Signals," K. C. Gross and

Y. Bao, Case Number SUN041050, U.S. Patent 7,391,835 (6/24/08).

"High-Accuracy Synchronization of Signals from Computer Systems," K. C. Gross and K. Vaidyanathan, Case

ID SUN080852, U.S. Patent 8,214,682 (Jul 3, 2012).

"Synchronizing Signals Related to Real-Time Prognostics of Enterprise Computer Systems," K. C. Gross and

K. Vaidyanathan, Case ID SUN080126, U.S. Patent 8,365,003 (Jan 21, 2013).

“Barycentric Coordinates Technique for Optimal Analytical Resampling of Quantized Signals,” S. Thampy, K.

C. Gross, and K. Whisnant, Case ID SUN050451, U.S. Patent 7,573,952 (Aug 11, 2009).

“Automated and Optimal Time-Series Resampling Process for Big Data IoT Applications,” K. C. Gross and G.

C. Wang, Case ID ORA18060722, Oracle Patent Pending (Oct 23, 2018).


UnQuantizeOracle-patented innovation that turns low-resolution input signalsinto high-accuracy output signals “up stream” of MSET2



Blue signals show the raw signals reported from 8-bit A/D chips used in most computing systems and for critical assets in many IoT industries.

Upper plot is a typical voltage, lower plot is a typical temperature.

The red signal shows the actual value of the variable being monitored.

For IoT assets with 8-bit A/D, Oracle has a proprietary “Moving Histogram” method to attain high-accuracy prognostics from low resolution sensors.

StarCat Core Voltage and Temperature Signals

Low-Resolution Signals Cause

ML Algorithms to fail at

Prognostics

Orale Confidential8/8/2019


Raw telemetry signals in many industries are quantized by low-resolution A/D chips.

In the example at right, the voltage signal is quantized to 10 mV “buckets” because of 8-bit A/D chips.

Oracle’s patented “UnQuantize” algorithm (in MSET2) reveals that this signal is slowly drifting due to a degrading interconnect.

8/8/2019

Quantized

Signals


Inferential SensingWith MSET2

High-Accuracy“Virtual Sensors”



MSET2 Disambiguates between Sensor Disturbances and Anomalies in Utility Critical Assets

– Sensors often have a shorter mean-time-between-failure (MTBF) than the assets the sensors are supposed to protect

– Oracle’s MSET2/SPRT detects all types of sensor de-calibration bias and sensor degradation modes

– MSET2/SPRT provides signal validation and sensor-operability validation. Subsequent system/process anomaly detection operations are then performed on fully validated signals


MSET2 for Inferential Sensing*

Oracle's high-end servers contain hundreds (sometimes thousands) of physical transducers (distributed temperature sensors, voltages, currents, and fan speeds) that protect the system by detecting when a parameter is out of bounds.

When a sensor failure is detected, MSET swaps out the degraded sensor signal, and swaps in an “analytical estimate” of the physical variable, called an "inferential sensor". This analytical estimate can be used indefinitely, or until the board containing the failed sensor needs to be replaced for other reasons.

No longer have to shut down a $1M critical asset to discover a $2 temperature sensor is drifting out of calibration.

Additional Use Case for IoT Customers:

Oracle’s Inferential Sensing also works very well for optimal imputation of missing values in customer’s real-time sensor time series.

*OracleLabs Patent: “Inferential Sensing for Enhanced Reliability, Availability, and Serviceability” U.S. Patent 7,292,952 (Nov 6, 2007).


SPRT Alarms

Inferential Sensors via MSET2

Physical sensors can fail. In many cases, the physical sensors have a shorter Mean Time Between Failure than the assets the sensors are supposed to protect.

With MSET, if a physical sensor fails or degrades in service, MSET can mask the sensor signal and swap in the MSET estimate (red variable in figure).

Immediate SPRT alarms observed.

Failed Sensor

MSET-2 disambuigates between sensor degradation mechanisms and degradation in IoT assets/processes


Remaining Useful Life

RUL



Oracle has developed a variety of innovative algorithms that leverage time series telemetry coupled with advanced ML pattern recognition (MSET and SPRT) for high accuracy estimation of Remaining Useful Life (RUL) of systems, components, and subsystems in business-critical and mission-critical environments.

RUL capability is a key enabler for Condition Based Maintenance (CBM) of customer assets.

RUL-based CBM is a structured preventative maintenance framework that significantly reduces operations-and-maintenance (O&M) costs for Oracle’s IoT and Big Data customers in the industrial sectors of Utilities, Transportation, Manufacturing, and Oil-and-Gas.

–-- Can use scheduled maintenance windows to prioritize proactive replacement of high-risk (shortest RUL) components

–-- Less unscheduled down time from short-RUL component failures in next operation cycle

Oracle Prognostics Innovations for Remaining Useful Life (RUL) Estimation for Critical Assets

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal43

Oracle Innovations for Remaining Useful Life (RUL)

Estimation with Quantitative Confidence Factors

"Remaining Useful Life Prediction Technique for Components Monitored by Telemetry," U.S. Patent 7,702,485 (Apr 20, 2010).

“Remaining Useful Life Stress-Based Prediction Technique for Systems Monitored by Telemetry,” U.S. Patent 8,340,923 (Dec 25, 2012).

"Detecting Degradation of Components Using Dynamic Telemetry Variables," U.S. Patent 7,283,919 (Oct 16, 2007).

"Detecting Degradation of Components During Reliability-Evaluation Studies," U.S. Patent 7,162,393 (Jan 9, 2007).

“Reliability Characterization of Components via Inferential Variable Surveillance,” U.S. Patent 7,216,062 (May 15, 2007).

“Quantitative Risk Index for Components Monitored by Continuous System Telemetry,” U.S. Patent 7,269,536 (Sept 11, 2007).

“Method and Apparatus for Generating the Operating Environment Time-Series for RUL of Critical Assets,” U.S. Patent 8,341,759 (Dec 25, 2012).

“Cooling Fan Wear-Out Indexing for Remaining Useful Life Estimation (RUL),” ORA180244, Oracle Patent-Pending (Jan 25, 2019).

“Power Transformer Real Time Prognostics and Bootstrapped Remaining Useful Life Estimation,” ORA180461, Oracle Patent Pending (Mar 7, 2018).

“Adaptive SPRT for Robust Remaining Useful Life Estimation,” ORA190174, Oracle Patent Pending (Feb 21, 2019).

“Remaining Useful Life Analysis with Integrated Irrelevance Filter for Utility Field Assets,” ORA190572, Oracle Patent Pending (May 2019)


Digital Twin for Prognostics



Ambient ParameterNormalization/Baselining

Oracle Patent 8,150,655

SPRTBinary Hypothesis

Test Engine

Digital Twin“GOLDEN SYSTEM”All new, thoroughlytested components

TelemetryTime SeriesSignatures

Operating Assets(Can be many in fleet)

YesNo

CONTINUE

SAMPLING

SET DATADISTURBANCE

FLAGS for all Affected

Operating Parameters

ResidualTime Series Signatures

ALERT?

PAIRWISEDIFFERENCEGENERATOR

TelemetryTime SeriesSignatures

MSETModel

Forecast ahead trajectory

signals from MSET

New Operating Regime?

Yes

Update Digital Twin knowledge

of its Real Twin

No

Oracle’s Pioneering “Digital Twin” Innovation for Advanced Prognostics of

Complex Engineering Assets (since 2003)(US Patent 7,171,586)


From Mission-Critical assets to Intelligent Asset Performance/Availability

Oracle MSET2 Adv Pattern Recognition transforms raw telemetry into actionable diagnostic/prognostic intelligence

Intelligent

Feedback/

Control

Process

Optimization

Automated

pattern

recognition

(MSET)Real-time diagnostic/prognostic flags

Pattern recognition results

Control

actuator

signals

Sensor

Signals

Environment

Metrics

Optimal Control Sensor Validation Failure Prediction

Customer's

Monitored

Assets

Real-time signal

Preprocessing.

Predictive

Alerts

Emergency

Subsystem

Shutdown-

Isolation

Proactive

Service

Request

Scheduling

Telemetry

Signature

Archive

Database


Oracle Cloud ImplementationData Flow Schematics



Customer Managed Customer ManagedOCS Managed

MSET-2 as a Service Functional Overview

MSET EngineUser Interface

Model Construction

Messaging (sending alerts to users)

Dashboard

Custom Add-Ons (optional)

Model Validation

48

Data Intake

Data Cleansing

Training

Validation

Alerts Presentation / Reporting

Data Storage

Data File

Data File

Data File

Data File

Sensor Data

Model Testing / Processing

Ops Integration

Mobile Apps

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |8/8/2019

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |10/27/15 Oracle Confidential – Internal/Restricted/Highly Restricted

Rigorous Process for ML Predictive Analytics Modeling Development, Sensitivity Optimization, and Validation


Summary: Oracle’s MSET2 and SPRT Prognostic Surveillance Algorithms Bring a Compelling Value Proposition for Dense Sensor Prognostic Applications

The ability to proactively catch very subtle incipient disturbances, even when the disturbance signature is a tiny

fraction of the inherent variance in the monitored metrics

Ultra-low False-alarm and Missed-alarm probabilities (Type-I and Type-II error probabilities)

Separately Specifiable False- and Missed-alarm probabilities [note: conventional equipment surveillance

approaches have a “sea saw” relationship between Type-I and Type-II error rates]

Real Time signal validation and sensor operability validation [note: most Type-I and Type-II errors in prognostic

health management monitoring of business-critical and even safety-critical systems are due to sensor

degradation events.]

Low compute cost for large-scale prognostic monitoring applications, i.e. lots of sensors and/or high sampling

rates. (In many past “bake off” comparisons between MSET2 and neural networks, MSET2 achieves an order of

magnitude higher sensitivity for catching subtle disturbances in noisy process variables, with an order of

magnitude lower compute cost)

Remaining Useful Life (RUL) estimation with quantitative confidence factors [note: RUL capability is a key

enabler for “Condition Based Maintenance” of customer assets]

Highly accurate “inferential variable” capability. (i.e. one doesn't have to shut down a million dollar asset

because a $2 internal sensor failed...MSET can swap in a highly-accurate inferential variable, so the sensor

fix/replacement can be postponed to a scheduled maintenance window).


Bibliography:Oracle External TechnologyPublications On MSET2 Innovations



BibliographySelected Scientific Publications on Oracle's Patented Real-Time Prognostic Innovations Based

on MSET and SPRT (2002 – 2019)

“Prognostics of Electronic Components: Health Monitoring, Failure Prediction, Time To Failure,” K. G. Gross, K. W. Whisnant and A. M. Urmanov, Proc. New Challenges in Aerospace Technology and Maintenance Conf. 2006, Suntec City, Singapore (Feb 2006).

"Electronic Prognostics Techniques for Mission Critical Electronic Components and Subsystems," K. C. Gross, K. W. Whisnant and A. M. Urmanov, Proc. 2006 Components for Military and Space Electronics Symposium, Los Angeles, CA, (Feb 2006).

“Proactive Detection of Software Aging Mechanisms in Performance-Critical Computers,” K. C. Gross, V. Bhardwaj, and R. L. Bickford, Proc. 27th Annual IEEE/NASA Software Engineering Symposium, Greenbelt, MD (Dec 4-6, 2002).

"Incipient Fault Detection in Storage Systems using On-Line Pattern Recognition" K. Vaidyanathan, K. C. Gross and R. Dhanekula, Proc. 60th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA (April 2006).

“Integration of Electronic Prognostics with Software Aging and Rejuvenation for Business-Critical Enterprise Servers,” K. C. Gross, 4th IEEE Intn'l Workshop on Software Aging and Rejuvenation, Dallas, TX (Dec 2012).

“Proactive Fault Monitoring in Enterprise Servers,” K. Whisnant, K. C. Gross and N. Lingurovska, Proc. 2005 IEEE Intn'l Multiconferencein Computer Science & Computer Eng., Las Vegas, NV (June 2005).

“Failure Avoidance in Computer Systems,” A. Urmanov and K. C. Gross, Proc. 59th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA (Apr 18-21, 2005).

“Proactive Detection of Software Anomalies through MSET,” K. Vaidyanathan and K. C. Gross, Proc. IEEE Workshop on Predictive Software Models (PSM-2004), Chicago (Sept 17-19, 2004).


"Electronic Prognostics Through Continuous System Telemetry," K. C. Gross, K. W. Whisnant and A. Urmanov, Proc. 60th Meeting of the

Society for Machinery Failure Prevention Technology, Virginia Beach, VA (April 2006).

"Remaining Useful Life Estimation of Computer Server Critical Components," D. J. Garvey, J. W. Hines, and K. C. Gross, Proc. 61st

Meeting of the Machinery Failure Prevention Technology (MFPT) Society, Virginia Beach, VA (April 2007).

“Predictive Analytics for Enhancing the Reliability, Availability and Serviceability of Enterprise Servers,” K. C. Gross, Proc. SmartSignal

Predictive Condition Monitoring Summit, Chicago, IL (Sept 2004).

"Functional Requirements for Predictive Analysis based on Supervised Learning for Automated Early Detection, Diagnosis, and

Prognosis of Service Deterioration for Clustered Application S/W and H/W Systems" by K. C. Gross and M. Zoll, Oracle Technical Due

Diligence Document (12/08/2011).

"Realtime Sensor Validation Technique for the Enhanced Reliability, Availability, and Serviceability of Enterprise Servers," A. Urmanov, B.

Guenin, K. C. Gross, and A. Gribok, 2004 Intn'l Conf. on Machine Learning; Models, Technologies and Applications (MLMTA'04), Las

Vegas, NV (June 21 - 24, 2004).

“A New Framework for Proactive Surveillance of Complex Networks of Entities,” A. Urmanov, A. Bougaev, K. C. Gross, and A. Gribok,

2004 Intn'l Conf. on Machine Learning, Models, Technologies and Applications (MLMTA'04), Las Vegas, NV (June 21 - 24, 2004).

“Improved Methods for Early Fault Detection in Enterprise Computing Servers,” K. C. Gross and K. Mishra, 2004 SAS Users Group

International (SUGI 29), Montreal, Canada. (May 9 – 12, 2004).

Bibliography (Cont’d)Selected Scientific Publications on Oracle's Patented Real-Time Prognostic Innovations Based

on MSET and SPRT (2002 – 2019)


Bibliography (Cont’d)Selected Scientific Publications on Oracle's Patented Real-Time Prognostic Innovations Based

on MSET and SPRT

“MSET Performance Optimization for Proactive Detection of Software Aging,” K. Vaidyanathan and K. C. Gross, Proc. 14th

IEEE Intn’l. Symp. on Software Reliability Eng. (ISSRE’03), Denver, CO (Nov. 2003).

Multivariate SPRT for Improved Electronic Prognostics of Enterprise Computing Systems," K. C. Gross and R. Dhanekula,

Proc. 65th Meeting of the Machinery Failure Prevention Technology Society (MFPT2012), Dayton, OH (April 2012).

“Novel Training Enhancements for Advanced Statistical Pattern Recognition Used for Electronic Prognostics of Enterprise

Computing Systems,” K. C. Gross, R. Dhanekula, and K. Vaidyanathan, Proc. IEEE World Congress in Computer Science,

Computer Engineering, and Applied Computing (WorldComp2011), Las Vegas, NV (Aug 2011).

“Utilizing Predictors for Efficient Thermal Management in Multiprocessor System-on-Chip Servers”, A. K. Coskun, T. S. Rosing

and K. C. Gross. IEEE Transactions on CAD of Integrated Circuits and Systems, (Nov 2009).

“Early Detection of Signal and Process Anomalies in Enterprise Computing Systems,” K. C. Gross and W. Lu, Proc. 2002 IEEE

Int’l Conf. on Machine Learning and Applications (ICMLA), Las Vegas, NV (June 2002).

“Advanced Pattern Recognition for Detection of Complex Software Aging Phenomena in Online Transaction Processing

Servers,” Karen J. Cassidy, Kenny C. Gross, and Amir Malekpour, Proc. Intnl. Performance and Dependability Symposium,

Washington, DC, (June 23rd - 26th, 2002).

“Monte Carlo Simulation of Telemetric Signals for Enhanced Proactive Fault Monitoring of Computer Servers,” K. Vaidyanathan

and K. C. Gross, Proc. 2005 Simulation Multiconference, Philadelphia, PA (July 2005).


Bibliography (Cont’d)

“Misspecification-Robust Sensor Validation in Computer Systems,” A. Urmanov and K. C. Gross, Proc. Nuclear Plant Instrumentation,

Controls and Human-Machine Interface Technologies (NPIC&HMIT 2004), Columbus, OH (Sept 2004).

“Proactive System Maintenance Using Real-Time Software Telemetry,” K. C. Gross, S. McMaster, A. Porter, A. Urmanov, and L. G.

Votta, in A. Osslo and A. Porter, editors, Proc. Remote Analysis and Measurement of Software Systems (May 2003).

“Watch Out For Thresholds in DataBase Query,” K. C. Gross, 17th Intn’l Symp. on High Performance Transaction Processing

(HPTS’17), Pacific Grove, CA (Oct 8-11, 2017).

“Machine Learning Innovation for High Accuracy Remaining Useful Life (RUL) Estimation for Critical Assets in IoT Infrastructures,” K. C.

Gross, D. Li, and A. Vakhutinsky, 19th Intn'l Conf. on Internet Computing and Internet of Things (ICOMP'18), Las Vegas, NV (July 30-

Aug 2, 2018).

“Combination of Unquantization Technique and Empirical Modelling for Industrial IoT Applications,” F. Zhang, S. Boring, J. W. Hines, J.

Coble, and K. C. Gross, 2017 American Nuclear Society Intn’l Conf., Washington D.C. (Nov 2017).

“KIDS Supervisory Control Loop with MSET Prognostics for Human-in-the-Loop Decision Support and Control Applications,” K. C.

Gross, K. Baclawski, E.S. Chan, D. Gawlick, A. Ghoneimy, Z.H. Liu, 2016 IEEE Intn’l Multi-Disciplinary Conference on Cognitive

Methods in Situation Awareness and Decision Support (CogSIMA) (Mar 2017).

"SimML Framework: Monte Carlo Simulation of Statistical Machine Learning Algorithms for IoT Prognostic Applications," A. More and K.

C. Gross, Proc. Intn'l Symposium on Internet of Things & Internet of Everything (CSCI-ISOT), Las Vegas, NV (Dec 15-17, 2016).

"SimSPRT-II: Monte Carlo Simulation of Sequential Probability Ratio Test Algorithms for Optimal Prognostic Performance," T. Masoumi

and K. C. Gross, Proc. 2016 International Symposium on Artificial Intelligence (CSCI-ISAI), Las Vegas, NV (Dec 15-17, 2016).



“Self-Adaptive Dynamic Decision Making Processes,” K. Baclawski, E.S. Chan, D. Gawlick, K. C. Gross, Z. H. Liu, 2016 IEEE Intn’l

Multi-Disciplinary Conf. on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) (Mar 2017).

“Framework for Ontology-Driven Decision Making”, K. Baclawski, E.S. Chan, D. Gawlick, A. Ghoneimy, K. C. Gross, Z.H. Liu, X. Zhang,

Journal of Applied Ontology, Vol 11, Issue 4, October 2017.

“MSET Prognostics for Operator Decision Aid for Human-in-the-Loop Supervisory Control Applications“Round-Robin Staggered-

Imputation (RRSI) Algorithm for Enhanced Real-Time Prognostics for Dense-Sensor IoT Applications,” K. C. Gross, K. Vaidyanathan, A.

Bougaev, and A. Urmanov, Intn'l Conf. on Internet Computing and Internet of Things (ICOMP'16), Las Vegas, NV (July 25-28, 2016).

“Advanced Pattern Recognition for Optimal Bandwidth and Power Utilization for Intelligent Wireless Motes for IoT Applications,” K. C.

Gross, K. Vaidyanathan, and M. Valiollahzadeh, 17th Intn'l Conf. on Wireless Networks (ICWN'16), Las Vegas, NV (July 25-28, 2016).

“Use Cases for Evaluation of Machine-Based Situation Awareness,” K. Baclawski_, K. C. Gross, E. S. Chan, D. Gawlick, A. Ghoneimy

and Z. H. Liu, 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Las Vegas, NV

(Apr 8-11, 2019).

"Telemetry Parameter Synthesis System for Enhanced Tuning and Validation of Machine Learning Algorithmics," Guang C. Wang and

Kenny C. Gross, IEEE 2018 Intn'l Symposium on Internet of Things & Internet of Everything (CSCI-ISOT), Las Vegas, NV (Dec 13-15,

2018).



“Real Time Empirical Synchronization of IoT Signals for Improved AI Prognostics,” Guang C. Wang and Kenny C. Gross, IEEE 2018

Intn'l Symposium on Computational Intelligence (CSCI-ISCI), Las Vegas, NV (Dec 13-15, 2018).

“Combining Advanced Machine Learning with Situation Awareness for Big Data Health Informatics Applications,” K. C. Gross and D.

Gawlick, 4th IEEE Intn'l Conf. on Health Informatics and Medical Systems (HIMS'18), Las Vegas, NV (July 30- Aug 2, 2018).

“MSET Plus Situation Awareness for Big Data Healthcare Prognostic Applications,” D. Gawlick and K. C. Gross, 2018 Analytics and

Data Summit, Oracle Customer Conference, Redwood Shores, CA (Mar 20-22, 2018).

“Forecasting Optimal Storm-Recovery Resource Allocation for Electric Distribution Networks,” P. Franklin, K. C. Gross, and A.

Vakhutinsky, 2018 Analytics and Data Summit, Oracle Customer Conference, Redwood Shores, CA (Mar 20-22, 2018).

“Machine Learning Innovation for High Accuracy Remaining Useful Life (RUL) Estimation for Critical Assets in IoT Infrastructures,” K. C.

Gross and D. Li, 19th Intn'l Conf. on Internet Computing and Internet of Things (ICOMP'18), Las Vegas, NV (July 30- Aug 2, 2018).

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