ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   06/20/2019
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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. 39 ID: Use Case Name: Machine Learning Driven Analysis of Batch Process Operation Data to Identify Causes for Poor Batch Performance
Application
Domain
Batch Manufacturing
Deployment
Model
On-premise systems
StatusPrototype
ScopeDetecting the issues in batch manufacturing process that leads to bad quality products or longer cycle times of batch processing
Objective(s)Provide insight to the operation team to improve the productivity of batch manufacturing through machine learning on historical operation data
Short
Description
(up to
150 words)
An approach was developed that can use machine learning models to identify issues in batch manufacturing.
Complete Description Batch operation is generally quite complex involving dynamics in the operation and interplay of various process variables. Due to this, sometimes, few batches end up running slower than nominal batch time and few batches also yield bad quality end products resulting in significant production loss. Additionally, often in the industrial context, data size and variety are limited and to develop a robust machine learning model from limited available data sets is a challenging task.
Due to transient nature of batch operation data, the traditional PCA algorithm fails in analyzing the batch data and hence MPCA was applied as logical extension of PCA algorithm. As MPCA naturally considers the dynamics in the data and inter-correlations among the process variables, it provides a valuable insight on the batch data.
The approach was successfully demonstrated on milk pasteurization process data where only 4 batches were provided for modelling. Using such 4 seed batches, the algorithm synthetically creates 50 batches of data and introduction of anomalies in some batches. Concept of design of experiments and stochastic perturbations are used in synthetic generation of the data set. The work was able to successfully build a robust MPCA model with such data and isolate the bad batches of data from good batches of the data. Additionally, through contribution plots, the algorithm identifies when a certain batch drifted from nominal operation and which variables are the root causes for the bad batch operation.
Stakeholders
Stakeholders'
Assets,Values
Systems'
Threats &
Vulnerabilities
Incorrect use of AI/ML; New Security Threats
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 Closeness to Golden Batch How close a process is to the best possible batch Helps in isolation of bad batches from good batches by identifying combination of process variable trajectories that lead to good or bad batch operation.
6 Success rate Very high success rate is required for the solution to be accepted.
AI Features Task(s)Classification
Method(s)Multiway Principal Component Analysis
Hardware64 GB RAM Windows server
TopologyNA
Terms &
Concepts Used
Classification, MPCA, Anomalies
Standardization
Opportunities
Requirements
Challenges
& Issues
Discovering actionable insight with limited industrial data set, handling dynamics in the process variables
Societal Concerns Description Consistent batch operation lead to enhanced productivity
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 Jeffy, F., J., Gugaliya, J., K., and Kariwala, V. Application of Multi-Way Principal Component Analysis on Batch Data, 2018 UKACC 12th International Conference on Control Published Use case taken from this source 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