Bosch Cd Test Data

[Natalie Omahony]

Goingthru the BME688 documents to generate the AI algorithm, I see the need for good training data for the sensor. This allows for a higher accuracy of the AI algo. Field or test data can then be analyzed versus the AI algo for detection of the compounds trained for.

Question here for the community is, is anyone familiar with a Laboratary that can provide the controlled data and has worked with the BME688 or similar sensor? We're looking to work with them to train the sensor to a higher accuracy.

Thank you for responding. We're interested in being able to train the sensor to detect various types of vape liquid smoke and cigarette/cigar smoke to a plausible degree of accuracy. If we can accomplish a plausible degree of accuracy, we're also looking for cooking gas (Hydrogen sulphide).

To enable engineers to rapidly and accurately interpret test data from measurement devices, test benches, and vehicles, Bosch developed ENValyzer (Engineering Test Data Visualizer and Analyzer), a MATLAB based tool for analyzing and visualizing measurement data.

Testing is one of the most critical phases of the engineering product development life cycle, and it demands enormous time and effort. At Bosch, products are subjected to various kinds of testing. Engineers must create test scenarios within the limitations of spreadsheets and other data postprocessing tools. The resulting measurement data comes in many formats, which are dictated by data acquisition software, test bench manufacturers, and other acquisition techniques.

Bosch engineers recognized several drawbacks with using disparate tools. First, the in-house tools required ongoing maintenance. Second, the amount of data the teams needed to process was growing beyond the limits of the tools. Third, the results the tools produced were not accurate enough to enable the engineers to precisely determine the quality of the component under test; in many cases, the data postprocessing software could not be enhanced with new features. Fourth, using the tools to configure and analyze the data involved numerous manual steps. Bosch wanted to develop and deploy a single platform for accurately analyzing and visualizing large amounts of engineering test data from a range of automotive systems.

The Bosch engineering tools team used MATLAB to develop ENValyzer, a tool that simplifies analysis without compromising integrity and helps engineers arrive at better decisions. The team used the object-oriented programming capabilities of the MATLAB language to simplify ongoing maintenance tasks, including the creation of more than 250 class definition files for the complete application.

Using MATLAB and MATLAB toolboxes, the team added several general-purpose analysis capabilities to ENValyzer, including functions for regression analysis, curve fitting, filtering, spectral analysis, data smoothing, and principal component analysis (PCA) calculations. They also developed MATLAB functions for domain-specific analysis.

The team added support for generating analysis and visualization reports in PDF, HTML, and Microsoft PowerPoint formats that show results in tables and plots. Users can create and customize reporting templates for various domains.

They used these new features to automate processes frequently performed by test and validation engineers in specific domains. For the steering group, for example, they added an ENValyzer function that performs filtering, smoothing, and other signal processing operations on steering angle, torque, and other measurement channels to automatically assess the quality of steering gears.

Bosch is currently using ENValyzer in production, and now offers the tool commercially to other companies via the MathWorks Connections Program and through other marketing forums. Bosch engineers in India, Germany, and North America use ENValyzer to evaluate common rail system and steering system data and to validate steering gear and fuel-level sensors.

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The fast-paced development of new and innovative AI-based concepts to increase efficiency, performance and reliability of Bosch e-drive systems and components lead to an increased demand in test bench measurement campaigns for training, calibration and evaluation.

The development and training of AI models based on test bench measurements involves significantly more resources compared to simulations, which leads to the strong need of automated trajectory generation and AI training to accelerate the development cycle from an initial AI-model definition via acquiring measurement training data to the deployment on the target hardware.

This causes new and very demanding requirements such as the automated and efficient operation of the test bench over long periods of time to acquire high-quality training data for the subsequent training of AI models such as digital twins.

The execution of a measurement campaign is currently a predominantly manual process, which involves considerable effort, experience, and constant monitoring which results in long dedicated sessions that causes high costs. Trajectories need to be manually planned, executed and analyzed carefully depending on the characteristics of the AI model. Moreover, it is crucial to ensure that the entire multidimensional operation area of the electric motor has been covered, which is a challenging task.

Another significant issue is the potential violation of safety limits, which leads to an immediate termination of the ongoing measurement cycles and subsequent cooling down times, hence delays of the entire measurement campaign.

Step 2 is based on the automation of the test bench to operate the input trajectories generated by the Safe Active Learning framework and returning the measured data via the same interfaces.

In Step 3 the Active Learning Client, uses the information of the latest measurement send by the test bench to update the internal probabilistic model, to calculate the next informative trajectory and to improve the AI model for the safety constraints.

Safe Active Learning enhances the test bench capabilities and avoids shutdowns by automatically planning and adapting new trajectories based on predefined safety constraints, thereby allowing for a safe automated day, night and even weekend operation. This significantly reduces the required measurement campaign time and optimizes the utilization of the test benches at the individual business units within Bosch.

First evaluations of test benches enhanced with Safe Active Learning capabilities show a significant reduction in required test bench time compared to the current conventional manual process of obtaining measurement data for subsequent model calibration or AI training. This guarantees a fast time-to-market for future AI models within the Bosch product portfolio.

The Smart Test Facility Automation based on Safe Active Learning enhances the capabilities of different test benches of various business departments. This innovative technology will contribute to the task of bringing AI into new domains and applications in multiple domains within Bosch.

I experienced two special moments in this project. One was when we first got the system running: Generating a safe trajectory, sending it via the Interfaces to the test bench, seeing the system starting to turn, measuring the trajectory and receiving it back at the Active Learning Client. The second moment was when I wanted to show my colleagues that the Safe Active Learning is running but I could not enter the control room of the test bench because it already was running unattended.

I really appreciate that we brought together a team with people of quite diverse backgrounds and skills, where everyone was interested and open to benefit from each other's knowledge. I think this shows the big advantage of the great competencies within Bosch. It was also great to see that we were able to bring a new methodology from basic AI research to life on our e-mobility test benches. And to be honest - the first evening when we turned off the lights in the test bench and went home while the AI was autonomously controlling the test bench was somehow a magical moment.

I enjoyed working with all the experts in this interdisciplinary team that has the clear focus on solving challenges by introducing new and innovative AI methods that cover the whole range from probabilistic models to the application and integration on the test bench.

As an interdisciplinary mathematician he designs mathematical algorithms that create impact in the domain. In his early career he worked together with systems biologists at Heidelberg and epidemiologists at Yale before joining the Bosch Center for Artificial Intelligence in 2017. Here, he combined his previous expertise in mathematical modeling, parameter estimation and experimental design with machine learning techniques to create innovation for Bosch.

Matthias is working as a Bosch Research engineer in the department for Advanced Vehicle Systems. He joined Bosch Research as a Ph.D. student in 2016. His research focuses on AI algorithms and databased methods with lead applications in the field of the electric powertrain. His responsibilities include the integration of databased methods like active learning to the test bench. Before joining the Smart powertrain team, he developed new powertrain concepts for electrified air mobility within Bosch Research.

IMPORTANT! You must include all data wiring going from the panel that shares a common connection with the point bus. For example, whatever terminals the point bus uses requires all wires on that terminal (i.e., D8112 keypads from Data In terminal) to be tested. They are all possible noise conductors.

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