ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   08/21/2019

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.
No. 40 ID: Use Case Name: Empowering Autonomous Flow meter control- Reducing time taken to -proving of meters-
Application
Domain
Manufacturing
Deployment
Model
Cloud services
StatusIn operation
ScopeCalibration of control devices
Objective(s)Reduce the time taken for trial & error methods to set the VFD and FCV setpoints
Short
Description
(up to
150 words)
The customer had to set VFD and FCV % manually to achieve desired flowrate using trial & error methods, which could take about 3-4 hours. Efficiency for the proving of the meters was very less & improvement was needed to remove any aberration in reading as it was time consuming.
Complete Description Cerebra was integrated with the system considering the flow of the fluid. The customer can choose between the available options of high flow rate, low flow rate or multi viscous flow. Then, with the master meter in the loop of testing, the meter from the field was introduced to analyse how much of aberration is there and then proving it more efficiently. Since it took more time for them to get the exact values of VFD & FCV % to achieve the desired flow rate, Cerebra’s Prognostics Engine was introduced. Purely based upon machine learning algorithms, the data models for the VFD & FCV % was used to predict the values to be chosen with an accuracy of about 98%. Since there was a presence of a closed-loop system, this predicted value was automatically registered on the valves’ monitors which only required small tweaking in the end, thus reduced human efforts.
StakeholdersProcess Industries; Humans
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities
Challenges to accountability, security threats
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 Model Accuracy Accuracy of the prediction model The extent to which the setpoints have correctly predicted
2 % Reduction in Calibration Time The amount of time saved from manually setting the calibration
AI Features Task(s)Prediction
Method(s)
HardwareApplication Server: 64 GB RAM/ 16 Core / 500 GB HDD; Data Server: 128 GB RAM/ 16 Core, 3 TB HDD
Topology
Terms &
Concepts Used
ISO 13379, 13381, 13374, 14224, 17359 , ISA-95
Standardization
Opportunities
Requirements
  • Mandate of the key sensors based on the type of equipment
    Based on the type of equipment, the makers need to have the basic set on sensors imbibed onto the system. E.g. for a pump – it is important to measure the input flow and output flow rates, vibrations, rotation speed, lube oil temperature and pressure. This will guide the equipment manufactures to provide their customers and their data products to capture the minimum required data and understand the equipment performance
  • Mandate for the organizations to expose the minimum and key parameters
    The equipment owners need to enable the basic set of sensors for the equipment health and performance which are required for monitoring the asset from any failures
  • Standards for Data Formats
    Each organization has a different way of capturing data and storing them in different formats. Due to which the solutions are not scalable across organizations though the product behind them is same. It takes customised efforts each time.
  • Guidelines for deciding the sampling frequency based on the type of data
    We see a need to have a specific set of guidelines to capture data at a minimum required sampling frequency. For example, a vibration sensor should capture data at least at 1 ms or less.
  • Guidelines for Feature Engineering
    There must be guidelines as to how the features need to be engineered for AI models. Lack of this would lead to more black box models not explaining how the models behave the way they do.
  • Guidelines for Standardization of event types and codes
    There are multiple events which occur for an asset or in a manufacturing plant. Guidelines would help people capture the data in a similar fashion helping the industry to benchmark against one another and at industry level we can understand, which events are the most critical.
    Guidelines for standardization of Fault and Error Codes for an equipment or process
    Similar to events, it is also useful to capture fault, failure and error codes in a standard way.
  • Process Guidelines for event related data (Maintenance and Work Orders)
    Guidelines would help people capture the data in a similar fashion helping the industry to benchmark against one another and at industry level we can understand, which events are the most critical
  • Guidelines for Training AI models
    A defined set of guidelines for AI models would be useful for the data scientists to follow. It will also aid the consumers of AI models to understand how the outcome has been deduced
  • Guidelines around AI model explainability
    With so many black box models floating around in the industry, it is difficult for consumers of AI models to understand then and their output. And with engineers and domain experts, coming into the picture, it is very much required to make these models more explainable.
  • Process Guidelines and methods for model evaluation (retraining)
    Before deployment and post deployment, it is very critical to have standard methods for models. And also post deployment, we must set guidelines for retaining the model on a periodic basis or based on data volatility. This is increasingly becoming important as AI models are being involved in more strategic and operational decision making.
  • Guidelines for disaster recovery and autonomous operations
    With the aid of AI models, the operations of an equipment or manufacturing plant are becoming more and more autonomous and self- sufficient. But the human monitoring is also important as any kind of inaccurate prediction can lead to a disaster and it is must to have some standard to recover from this situation and to assess the conditions to go for autonomous operations.
Challenges
& Issues
Societal Concerns Description Promoting sustainable industries, and investing in scientific research and innovation, are all important ways to facilitate sustainable development.
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 Web Page Accelerating shale production through digital technology integration Published Use case taken from this source Flutura Business Solutions Pvt. Ltd., TechnipFMC Link
2 Web Page Fundamentals of meter provers and proving methods Published Fundamental definition of Meter Provers Flow Management Devices 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