ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   04/24/2019
Home

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. 45 ID: Use Case Name: Anomaly Detection in Sensor Data Using Deep Learning techniques
Application
Domain
Maintenance & support
Deployment
Model
Hybrid or other (Cloud or on premise deployment)
StatusPoC
ScopeTemporal Data captured from sensors
Objective(s)Identify Anomalies and Events by learning the temporal patterns of sensor data, based on Deep Learning techniques
Short
Description
(up to
150 words)
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behaviour and health of the machine. The sensors temporal data has several complex patterns that are very hard to identify with traditional methods. We have proposed the use of Deep Learning algorithms for analysing such temporal patterns for anomaly/event detection, diagnosis, root cause analysis.Algorithms proposed so far are LSTM-AD, EncDec-AD, online RNN-AD. We used industrial datasets wherever possible and publically available datasets in other scenarios. In most of the cases, our algorithms were significantly better than other methods.
Complete Description Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behaviour and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies/events in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies.
LSTM-AD
Our Work started with Stacked LSTM network which is trained on non-anomalous data and used as a predictor over a number of time steps. The resulting prediction errors are modeled as a multivariate Gaussian distribution, which is used to assess the likelihood of anomalous behavior. The efficacy of this approach was demonstrated on four datasets: ECG, space shuttle, power demand, and multi-sensor engine dataset.
EncDec-AD
As an extension to the prior work we proposed a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct normal time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experimented with three publicly available quasi predictable time-series datasets: power demand, space shuttle, and ECG, and two real-world engine datasets with both predictive and unpredictable behavior. We had shown that EncDec-AD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we showed that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).
Online-AD
The common approach of training one model in an offline manner using historical data is likely to fail under dynamically changing and non-stationary environments where the definition of normal behavior changes over time making the model irrelevant and ineffective. We described a temporal model based on Recurrent Neural Networks (RNNs) for time series anomaly detection to address challenges posed by sudden or regular changes in normal behaviour. The model is trained incrementally as new data becomes available, and is capable of adapting to the changes in the data distribution. RNN is used to make multi-step predictions of the time series, and the prediction errors are used to update the RNN model as well as detect anomalies and change points. Large prediction error is used to indicate anomalous behaviour or a change (drift) in normal behaviour. Further, the prediction errors are also used to update the RNN model in such a way that short term anomalies or outliers do not lead to a drastic change in the model parameters whereas high prediction errors over a period of time lead to significant updates in the model parameters such that the model rapidly adapts to the new norm. We demonstrate the efficacy of the proposed approach on a diverse set of synthetic, publicly available and proprietary real-world datasets.
StakeholdersMaintenance and support functions, Monitoring, Procurement
Stakeholders'
Assets,Values
Systems'
Threats &
Vulnerabilities
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 Precision Correctly Predicted Anomalous scenarios/ Total Anomalous scenarios predicted
2 Recall Correctly Predicted Anomalous scenarios /Total Anomalous Scenarios
AI Features Task(s)Prediction
Method(s)Deep Learning
Hardware
Topology
Terms &
Concepts Used
Deep Learning, LSTM, encoder-decoder, Temporal data
Standardization
Opportunities
Requirements
Challenges
& Issues
  • Sensor data collection
  • Noisy Data
  • Data with missing temporal features
  • Rarity of Anomalous Data
Societal Concerns Description
SDGs to
be achieved
Industry, Innovation, and Infrastructure
Data Characteristics
Description Multiple datasets(publically available, real industrial) were used
Source
Type Temporal data
Volume (size)
Velocity
Variety Space shuttle, ECG, Engine, Power demand
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 Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection, Link
2 Conference
Proceedings
Sakti Saurav, Pankaj Malhotra, Vishnu TV, Narendhar Gugulothu, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, Online anomaly detection with concept drift adaptation using recurrent neural networks, CoDS-COMAD '18, Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, Goa, India January 11 - 13, 2018 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