LHP Data Analytics

Engine Diagnostics: Emissions Fault Analysis

Executive Summary

Michael King

President, Data Analytics & IoT

LHP Engineering Solutions

http://LHPES.com

Engine Diagnostics Analysis: Background

• Cummins Emission Solutions (CES) focuses on the design and development of aftertreatment systems designed to meet stringent emissions requirements as well as customers expectations for high quality.

• In preparation for the 2017 product launch, CES requires reactive and proactive monitoring of system performance to identify trends or characteristics of performance so true failures can be addressed before they occur.

• One such data source that may be underutilized to identify trends in performance is engine ECM images captured by the Cummins INSITE service tool during each service event. These images are then stored in a Cummins database.

• CES wishes to explore the ‘art of the possible’ regarding what trends can be identified by interrogating this database on a pilot basis through the services of experts in LHP Data Analytics.

Engine Diagnostics Analysis:

Problem Statement

1. Tell us [Cummins] what we already know

2. Provide Fault Code based analysis of historical INSITE images

3. Provide expertise around opportunity and how to use Machine Learning with Cummins’ data

4. Provide future opportunities, analytical techniques, proactive analysis, and support for next steps

Phase I: Develop Baseline Data Analytics

• 2016 ISX Engines– INSITE Images

• Faults & Parameters– 1682/5655 and 2771 Faults and Fault Parameters

– 1682/5655 and 2771 Aftertreatment History Parameters

Phase II: Establish Reactive, Proactive, and Predictive Analytics

• Expand engine population to include 2017 test

• Incorporate Data Loggers, manufacturing data, and additional data sources

Phase III: Deploy “Gold Standard” Analytics for 2017 Products

• Global deployment of common analytics

• Establish training and support plan

Engine Diagnostics Analysis:

Project Scope

• Leveraged existing Cummins ECM INSITE image database

• Developed Fault-to-Analyze cross reference

• Established Parameter cross references

• Developed Engine Hours logic across the data set

• Calculated First Time to Failure

• Performed Lead/Lag analysis

• Deployed R graphical capabilities for advanced analysis

• Future logic enhancements– Nature of generating an INSITE image

– Correlate odometer reading to service bay mileage reading to see how long an operator drove with the fault indicator

– Additional value added data sets and their measures

LHP Data Analytics Methodology

• Mean time to First Fault and All Faults are increasing

– Average of 2.98 days to first Fault

– Average of 6.50 days to first SCR Fault

• General trend is improving for % Engines with Faults, Days to First Failure

• Potential issues related to:

• Kenworth Mexico

• MDC Shipments

• ISX2 2013

• Software Calibration Phase

Engine Diagnostics Analysis:

Initial Results

• Incorporate Data Loggers to provide real-time data

– Expand Machine Learning and Predictive Analytics

– Provide geographic and climate location

• Expand engine data population, incorporate manufactured data

– Epidemiology: Tie to part failures / manufacturing failures / lot numbers

– Expanded pre-Fault Lead/Lag analysis

• Integrated Marketing and Warranty programs

– ESN to VIN to customer for advanced notifications for engines at risk

– Validate INSITE images against claims (three level match)

• Incorporate ECM hardware and software versioning

– When the ECM was flashed, which version

– Tie to any software issues

• Engineering thresholds and real time data

– Abort Fault conditions, operating ranges

Engine Diagnostics Analysis:

Next Steps

Highlighted Engine Diagnostics Analysis

Aftermarket Faults

• Mean time to initial Faults is high after initial launch– Average of 2.98 days to first fault

– Average of 6.50 days to first SCR fault

– General trend improves after 6 months:• % Engines with Faults

• Days to First Failure

Aftermarket Faults

• Overall trends are improving for 2016 ISX

• Analysis identified Potential issues related to• Kenworth Mexico

• ISX2 2013

• Deeper dive into Kenworth Mexico to see their issues and why they were an outlier:• Issues early in engine

build/release

• Issues starting to reappear in October

Kenworth Mexico

Aftermarket Faults Selected

Fault To Analyze = 2771

• Two undesirable insights

• Lots of Faults for engines shipped to MDC

• MDC engines also have high severity Faults

• Identification of lead/lag analysis for preceding Faults

Fault To Analyze = 2771

Parameter Analysis

• Several parameters indicate failures occur:

• Early in lifecycle

• At low temperature thresholds

Calibration Software Deep Dive

• Isolating Calibration Software root cause to failure:• Specific version of

Calibration Software has significantly more Faults as a % of their installed population than others

Engine Diagnostics: Executive Dashboard

Engine Diagnostics: Fault Snapshot

Engine Diagnostics: Lead-Lag Analysis

Engine Diagnostics: Fault Code Deep Dive

Machine Learning Capability

• Identifies key parameters that are leading to specific Fault Codes

• Mutual Information identified unexpected potential variables to analyze

• Provides rapid Feature selection and down selecting or reducing data

• Final parameter list to investigate in more detail

LHP Data Analytics Solutions

• Technical and Analytics

• James Roberts

• Vice President, Data Analytics

Solutions

• James.Roberts@LHPES.com

• 812.314.7921

• Michael King

• President, Data Analytics

Solutions

• Michael.King@LHPES.com

• 812.341.8460

• Account Management

• Paul Wright

• Director, Business Development

• Paul.Wright@LHPES.com

• 812.314.7920