ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   07/21/2019
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The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
No. 51 ID: Use Case Name: Machine Learning Tools in Support of Transformer Diagnostics
Application
Domain
Performance evaluation and diagnostics
Deployment
Model
Prototype
StatusUnder development
ScopePower Transformers operation and maintenance
Objective(s)Use of Machine Learning (ML) algorithms as supporting tools for the automatic classification of power transformers operating condition
Short
Description
(up to
150 words)
The successful use of ML tools may find multiple applications in the industry such as providing fast ways of analysing new data streaming from online sensors, evaluating the importance of individual variables in the context of transformer condition assessment and also the need or adequacy of data imputation in the so widely common problem of missing data
Complete Description The work consists of training 12 ML algorithms with real data from 1,000 (one thousand) transformers that were individually analyzed by human experts. Each transformer in the database is scored with a ‘green’, ‘yellow’ or ‘red’ card depending on the data, the interpretation of human experts, or even after some calculations carried out by the company’s internal algorithms frequently utilized by the experts to identify units with technical operational issues.
The ML algorithms, however, do not utilize or are given any of the engineering tools employed by the human experts. The algorithms only employed the raw data in a supervised learning process in which a column named ‘Class’ was added to the transformer information with the classification red, yellow or green provided by the human expert.
StakeholdersTransformers end users
Stakeholders'
Assets,Values
Systems'
Threats &
Vulnerabilities
Lack of enough data to perform the analysis
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 Algorithm accuracy Output when compared to the human expert analysis of the same data See Reference
AI Features Task(s)Statistical learning
Method(s)12 ML methods used for the comparison exercise:
Linear Algorithms
  1. General linear regression (logistic regression) - GLM
  2. Linear discriminant analysis - LDA
Non-linear Algorithms
  1. Classification and regression trees (CART and C5.0)
  2. Naïve Bayes algorithm (NB)
  3. K-Nearest Neighbor (KNN)
  4. Support Vector Machine (SVM)
Ensemble Algorithms
  1. Random Forest (stochastic assembly of a large number of CART algorithms)
  2. Tree Bagging (Tree Bagging)
  3. Extreme Gradient Boosting Machine (xGBM1 and xGBM2)
  4. Artificial Neural Networks (ANN)
HardwareStandard laptop
TopologyNA
Terms &
Concepts Used
Machine Learning Algorithms, Transformer Diagnostics, Condition Assessment, Automated Tool
Standardization
Opportunities
Requirements
Challenges
& Issues
Data availability, missing data, imbalanced classes
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






Scenario Name Training
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 Cheim, Luiz V., Machine Learning Tools in Support of Transformer Diagnostics, Cigre General, Session Paris 2018, paper reference A2-206 Presented in August 2018 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