Getting value out of the Nissan dataset

Table of Contents
Project Summary
Explanation of the Brake Model
Nissan’s Data Set
Summary of existing dataset
Applying Pitstop Brake Model & how it works
Success & Validation
Conclusion
Next steps / Phase 2 to further prove out the model
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Summary: 3 Key takeaways from Phase 1
Brake model is working
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Comparison to mileage based shows a distinct advantage
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Clear next steps to Achieve…. _____
Next steps / Phase 2 to further prove out the model

Secret Sauce: Combining telematics, service records with big data and machine learning for example: (i) reliably detect all braking events, (ii) manage cohorts to create correct statistical distributions for energy and for brake maintenance records (iii) Validating the model against maintenance records and known replacements
TL;DR the existing dataset can be used for a brake model
From the existing list of Pitstop prognostic models, it seems that the brake model would be the most applicable to the Nissan dataset as it stands.
How The Brake Model Works
Problem: If brakes wear out it is a safety and regulatory issue, but inspections mean downtime and expense
Em = kinetic energy of motion, where m = vehicle mass and V = speed of vehicle
Brakes wear because vehicles must dissipate (convert to heat) their energy of motion Em
The vehicles dissipating the most energy are wearing out their brakes fastest and should be targeted for inspection

TL;DR the existing dataset can be used for a brake model
From the existing list of Pitstop prognostic models, it seems that the brake model would be the most applicable to the Nissan dataset as it stands.
How The Brake Model Works
Problem: If brakes wear out it is a safety and regulatory issue, but inspections mean downtime and expense
Secret Sauce:
Combining telematics, service records with big data and machine learning for example: (i) reliably detect all braking events, (ii) manage cohorts to create correct statistical distributions for energy and for brake maintenance records (iii) Validating the model against maintenance records and known replacements
Em = kinetic energy of motion, where m = vehicle mass and V = speed of vehicle
Brakes wear because vehicles must dissipate (convert to heat) their energy of motion Em
The vehicles dissipating the most energy are wearing out their brakes fastest and should be targeted for inspection

Steps required to track Brake Wear
Detect when braking events occur.
Calculating a metric of brake usage per vehicle - energy dissipation per unit distance driven (called the dissipation value).
Creating a frequency distribution of the above metric
Creating a distribution of brake services as a function of mileage driven
Mapping between the distributions to get an estimated mileage for brake
Replacement given the dissipation value
For more in depth information: Paper on Brake Wear Model

Detect when braking events occur.
Calculating a metric of brake usage per vehicle - energy dissipation per unit distance driven (called the dissipation value).
Creating a frequency distribution of the above metric
Creating a distribution of brake services as a function of mileage driven
Mapping between the distributions to get an estimated mileage for brake
Replacement given the dissipation value
Recommendation to extract further value

High resolution data from a small volume of vehicles (Engineering test fleet)
Measurements of physical components every week/month (brakes, tires)
CAN bus data including detailed attributes like brake pressure
GPS & Acceleration data at high frequencies (~1s or faster)
Speed, power terrain parameters; torque, coolant, engine oil temp, temp throttle position amongst others (~1s)
High mileage in short periods of time
The data has good attributes for Brake Predictions
Consistent datastreams from large volumes of vehicles (Customer vehicles)
GPS & Acceleration data at low frequencies (~30s)
Maintenance records includes brake measurements
Big Data Volume! Thousands of vehicles with more than 2 brake measurements.

High resolution data from a small volume of vehicles (Engineering test fleet)
Trip data does not add up to the total mileage driven. Ex. CTB531 has 10,000 km of accumulated mileage between the first brake measurement and last but there is only ~5000 km’s worth of trip data
There is not enough data volume, both length of time or number of vehicles to perform any meaningful accuracy/validation calculations
There are cases where either dates, or pad measurements are inconsistent. ex. brake pads increase in thickness over time based on the data
The data has some challenges for Brake Predictions
Consistent data streams from large volumes of vehicles (Customer vehicles)
30 second sampling frequency can miss out on relevant brake events, making the dissipation calculation less accurate
Service data dates and odometers don’t match up always. Sometimes we see reducing mileage over 1 year which signals incorrect data entry.

Expectation is satisfied with engineering test fleet which is that more energy dissipation in brakes => more wear between measurements (seen in pad thickness measurement) (CTB546)
Applying the brake model - exploration on FET data

Front Left Inner
Front left Outer
Front Right Inner
Front Right Outer
Rear Left Inner
Rear left Outer
Rear Right Inner
Rear Right Outer
Green line is the expected slope
Note: Higher dissipation values are to the left (dissipation is negative by convention)
Note: Data Timespan ~4 months
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-7E=09
-6E=09
-5E=09
-4E=09
-3E=09
-2E=09
-1E=09
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The brake model is showing Success & validation
Showcase accuracies and strong signs of success with the available dataset
Improvements of the model are better described as reliability rather than accuracy, since it means the model can be adjusted to avoid incorrect assumptions about different vehicle cohorts. However, if we think of accuracy as an average measure of agreement, such as R2, it will amount to the same thing.
Accuracy is not the same as precision. For example, it does not matter if measurements are made to the nearest 100 μ if the standard deviation of the measurement is 1.0 mm.

Next steps to further prove out the brake model
High resolution data helps create accurate dissipation models. However to take advantage of the cohorts via big data there is not enough cases (< 20). This serves as a great start to show that energy dissipation directly correlates with brake wear (slide 7).
However to be statistically relevant a validation test needs to incorporate more cases. The low resolution UIO data helps to put vehicles in cohorts and then plot them on a distribution. An R^2 measure can be made between each vehicle and the “average”. The average is defined as the mileage suggested brake replacement that is provided to every customer.
The accuracy will be the error between the algorithms estimated brake replacement and the average case.

Step 1: calculate the dissipation for each vehicle and assign it to a cohort
Steps to validate the model
Cohort distribution
Expected brake wear at mileage for =-1800
Table 1.
Table 2.
Table 3.
Expected brake wear at mileage for =-1000
Step 2: Each cohort will have a wear pattern which can estimate when a brake pad replacement will be needed. Note: vehicles can change between cohorts as additional data is captured

Validate the model
Step 3: Comparison between each cohort (blue dotted line) and the average (orange dotted line) will provide an accuracy measure. Cohorts that experience more wear will benefit from safety whereas those that experience less wear will benefit from receiving an accurate suggestion.

Alert would be early. This leads to customer trust issues. “ The dealer just wants me to do service that I don't need”.
Unsafe suggestion that would be too late. Could lead to an accident because of low brakes
64,000 KMs brake replacement suggested

Alert would be early. This leads to customer trust issues. “ The dealer just wants me to do service that I don't need”.
Unsafe suggestion that would be too late. Could lead to an accident because of low brakes
64,000 KMs brake replacement suggested

We expect phase 2 will prove that the brake model works on the UIO data and be able to showcase a percentage accuracy.
We will use the validation technique described in figure 9 (slide 9).
Based on Pitstops current brake model it seems the accuracy should be within this range x-y% which would be the target.
Summary: 3 Key takeaways from Phase 1

We expect phase 2 will prove that the brake model works on the UIO data and be able to showcase a percentage accuracy.
We will use the validation technique described in figure 9 (slide 9).
Based on Pitstops current brake model it seems the accuracy should be within this range x-y% which would be the target.
Summary: Expected conclusion of phase 2

Nissan Roadmap to Additional Predictions

Table of Contents
Pitstop’s current Models
How the Pitstop data engine works
Current Pitstop Models / Data Requirements
Custom Models - to solve specific problems
What Data Nissan Has today:
Positive attributes and what can be done with it today
Challenges & Gaps
Recommendations Priorities for how to fill the data gap
Suggested Road Map
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Battery
Remove no start scenarios
Reduce electrical failures Examples include: Battery, Alternator, Starters, Parasitic loads etc..
Additional Algorithm Details
Engine Control
Improve Fuel Efficiency
Manage Engine Fault Priorities
Examples include: Spark plug, Wires, Injectors, Timing, Crank sensor, O2 sensor, Exhaust, Water-pump etc..
Emissions
Reduce Diesel Lockouts
Maintain emissions system before catastrophic failures
Examples include: DEF, DPF, EGR, Air filter, Hose leaks, Pressure leaks, EVAP issues, Turbo leaks etc..
Brakes
Improve vehicle safety
Brake wear analysis across entire fleet
Examples include: Brake pads, Rotors, hydraulic, pneumatic etc..

Custom Algorithm Example
Problem: Delivery Van Sliding Door was not intended to open and close 100’s of times per day - causing bracket failure and eventually body panel damage
Solution: Utilizing a couple readily available telematics PIDs and repair order information, Pitstop can create a custom algorithm to predict when this failure will occur -avoiding a significant body panel repair cost

Additional Algorithm Details
Problem
Delivery Van Sliding Door was not intended to open and close 100’s of times per day - causing bracket failure and eventually body panel damage
Solution:
Utilizing a couple readily available telematics PIDs and repair order information, Pitstop can create a custom algorithm to predict when this failure will occur -avoiding a significant body panel repair cost

High resolution data from a small volume of vehicles (Engineering test fleet
Measurements of physical components every week/month (brakes, tires)
CAN bus data including detailed attributes like brake pressure, Speed, power terrain parameters;torque, coolant, engine oil temp, temp throttle position amongst others
GPS & Acceleration data at high frequencies (~1s or faster)
High mileage in short periods of time
Consistent datastreams from large volumes of vehicles (Customer vehicles)
GPS & Acceleration data at low frequencies (~30s)
Maintenance records as long as the customer arrives at the dealer
Big Data Volume! 10’s of thousands of vehicles
The Nissan data has good attributes for models

The dataset consists of telematics generated and service data
acceleration, gps at 30 second intervals and odometer
Service records from 30K or so vehicles.
With the current state of telematics data alone solutions related to route optimization and driver risk can be implemented.
With service data alone can assist with getting ahead of defects or looking at inventory and service lane statistics. You can build mileage based prediction models as well.
A value item to be extracted from both data sets is a brake model!
Additional models that maybe extracted include brake and tire wear. These will require extensive analysis and research before being certain that the reliability and accuracy of the models are suitable.
The dataset overall does have challenges & gaps

Start by asking what types of value propositions are most important to the market.
For example if it’s clear that Nissan wants to have models for as many components as possible, then the strategy requires deep edge to cloud implementation. This is capability Pitstop has in the market.
If Nissan decides they want to focus on brakes, batteries and tires then the roadmap will just require specific time-series sensors to be enabled in the data stream.
Pitstop suggests taking a fully integrated approach in order to take advantage of rapid software and data science iteration cycles. New problems will emerge that you cannot currently predict, and hence you need a flexible infrastructure to quickly build new models. This will payback returns as customer satisfaction will improve as well as reduction of recall and warranty costs.
Recommendation to extract further value

Start by asking what types of value propositions are most important to the market.
For example if it’s clear that Nissan wants to have models for as many components as possible, then the strategy requires deep edge to cloud implementation. This is capability Pitstop has in the market.
If Nissan decides they want to focus on brakes, batteries and tires then the roadmap will just require specific time-series sensors to be enabled in the data stream.
Recommendation to extract further value
Pitstop suggests taking a fully integrated approach in order to take advantage of rapid software and data science iteration cycles. New problems will emerge that you cannot currently predict, and hence you need a flexible infrastructure to quickly build new models. This will payback returns as customer satisfaction will improve as well as reduction of recall and warranty costs.

Start by asking what types of value propositions are most important to the market.
For example if it’s clear that Nissan wants to have models for as many components as possible, then the strategy requires deep edge to cloud implementation. This is capability Pitstop has in the market.
If Nissan decides they want to focus on brakes, batteries and tires then the roadmap will just require specific time-series sensors to be enabled in the data stream.
Recommendation to extract further value

Recommendation to extract further value
67%

Start by asking what types of value propositions are most important to the market.
For example if it’s clear that Nissan wants to have models for as many components as possible, then the strategy requires deep edge to cloud implementation. This is capability Pitstop has in the market.
If Nissan decides they want to focus on brakes, batteries and tires then the roadmap will just require specific time-series sensors to be enabled in the data stream.
Recommendation to extract further value

Start by asking what types of value propositions are most important to the market.
For example if it’s clear that Nissan wants to have models for as many components as possible, then the strategy requires deep edge to cloud implementation. This is capability Pitstop has in the market.
enabled in the data stream.
Recommendation to extract further value

Recommendation to extract further value

Recommendation to extract further value