ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications   04/27/2024

Editor's comments and enhancements are shown in green. [✓ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 38 ID: Use Case Name: Machine learning driven approach to identify the weak spots in the manufacturing of the circuit breakers.
Application
Domain		Manufacturing
Deployment
Model		Prototype
StatusOn-premise system
ScopeDetecting the issues in manufacturing process that leads to early failures of the circuit breakers through the data mining of the manufacturing process.
Objective(s)To generate actionable intelligence to improve the manufacturing process of circuit breakers through mining of manufacturing related data.
Short
Description
(up to
150 words) 		An approach was developed that can mine the manufacturing data of circuit breakers through multiple machine learning algorithms. The approach could successfully identify the weak spots in the manufacturing where failure rate jumped from 0.2% to 7% (35 fold more probability of failure) and hence candidates for improvement in the manufacturing process.
Complete Description High voltage circuit breakers are critical component of an electric circuit and it has a normal lifespan of 30-40 years. However, due to various reasons few circuit breakers fail within 0-5 years of operation. As a manufacturer of these circuit breakers, lots of data related to manufacturing aspects are present with the manufacturer. Such data has information about production lot size, material of production, design voltages for sub-components, heater voltages, date of failure etc. In general data related to 49 variables are captured for close to 56000 circuit breakers over a lifespan of several years. The manufacturer is interested to know if there are any weak spots in the manufacturing process which leads to higher failure rates.
Circuit breakers can fail not only due to manufacturing defects but also due to wrong operation of the circuit breaker in the field e.g. applying voltages higher than design values. However, operational data of the circuit breakers was not available with the manufacturer.
Therefore, the key challenge of this project was knowledge discovery with partial data set using machine learning algorithms.
The data scientists applied various machine learning algorithms such as decision tree, random forest, support vector machine, Naïve Bayes classifier, logistic regression and neural network and compared the results of one algorithm verses the other algorithm. Through multiple numerical experimentations on data selection and algorithm hyper parameter tuning, the data scientist team selected the best algorithms and deduced the key weak spots in the manufacturing that are generally associated with high failure rates. In conclusion, the work provided a set of 5 actionable rules, where the failure rates jumped drastically from 0.2% to 7% leading to 35-fold higher chance of failure.
StakeholdersBatch manufacturer such as milk pasteurization, pharmaceutical, paint manufacturing, etc.
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities		Incorrect use of AI/ML
Performance
Indicators (KPIs) 		Seq. No. Name Description Reference to mentioned
use case objectives
1 Ratio of ML discovered failure rate to nominal failure rate What combination of manufacturing processes/decisions leads to higher failure rates compared to nominal failure rate Actionable intelligence to improve the manufacturing process of HV circuit breakers
5 Operational efficiency Cycle time is the primary measure in manufacturing industry.
AI Features Task(s)Classification
Method(s)Decision trees, SVM, ANN, Logistic Regression, Random Forest and Naïve Bayes
Hardware64 GB RAM Windows server
TopologyNA
Terms &
Concepts Used		Classification, Actionable Rules, HV Circuit breakers
Standardization
Opportunities
Requirements		Standardization of data representation models comprising of both manufacturing related data and end-use related data.
Challenges
& Issues		Discovering actionable insight with partial data set and managing bias in ML models due to limited number of failed cases
Societal Concerns Description Safe and reliable power delivery
SDGs to
be achieved 		Industry, Innovation, and Infrastructure
Data Characteristics
Description
Source
Type
Volume (size)
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name 		Scenario
Description 		Triggering Event Pre-condition Post-Condition






Training Scenario Name:
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case 		Originator
Organization 		Link
1 Conference Kumar, S., K., Jamkhandi, A., G., and Gugaliya, J., K., Achieving Manufacturing Excellence through Data Driven Decisions, IEEE International Conference on Industrial Technology, Melbourne Australia PP 1267-1273 Presented in Feb 2019 Use case taken from this reference ABB Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013