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:
 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.)
 Functional aspects, trustworthiness, and societal concerns
 AI life cycle components include acquire/process/apply.
Use Case Name:
Empowering Autonomous Flow meter control- Reducing time taken to -proving of meters-
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.
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.
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.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
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:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 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.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 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