Use of machine learning on SCADA data for asset's prognostics health management

2020 ◽  
Vol 60 (2) ◽  
pp. 602
Author(s):  
Alexandre Cesa ◽  
Elliot Press
Keyword(s):  
Machine Learning ◽  
Health Management ◽  
Analytical Techniques ◽  
Root Cause Analysis ◽  
Machine Learning Techniques ◽  
Safe Operation ◽  
Cause Analysis ◽  
Root Cause ◽  
Learning Techniques ◽  
Learning Frameworks

The timely detection of anomalies in the process industry is paramount to ensure effective and safe operation of plant. There typically exists an abundance of historical data recorded in Supervisory Control and Data Acquisition (SCADA) systems, which is most often used for understanding past events through, for example, root cause analysis. It is envisaged that higher levels of insight could be achieved from the same datasets by utilising more advanced analytical techniques such as machine learning frameworks. This would enable moving from a ‘diagnosis–mitigation’ (i.e. a root cause analysis) paradigm to a more desirable ‘detection–prediction–prognosis–prevention’ paradigm. Machine learning techniques can be used on SCADA data to support the detection of plant anomaly conditions that do not necessary manifest as process alarms for example. We used a Bayesian network framework on the Tennessee Eastman Plant benchmark problem to demonstrate the technique’s capability. Our model proved to be effective in detecting anomalous plant conditions in most situations.

scholarly journals Lifelong Machine Learning and root cause analysis for large-scale cancer patient data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Gautam Pal ◽  
Xianbin Hong ◽  
Zhuo Wang ◽  
Hongyi Wu ◽  
Gangmin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lifelong Learning ◽  
Root Cause Analysis ◽  
Cause Analysis ◽  
Training Time ◽  
Root Cause ◽  
Random Decision Forest ◽  
Batch Data ◽  
Over Time ◽  
Decision Forest

Abstract Introduction This paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimensional data from scratch is computationally infeasible since constant data stream ingestion on top of a historical data pool increases the training time exponentially. Therefore, the need arises on how to retain past learning and fast update the model incrementally based on the new data. Also, the current machine learning approaches do the model prediction without providing a comprehensive root cause analysis. To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) model. Case description A cancer patient’s pathological tests like blood, DNA, urine or tissue analysis provide a unique signature based on the DNA combinations. Our analysis allows personalized and targeted medications and achieves a therapeutic response. Model is evaluated through data from The National Cancer Institute’s Genomic Data Commons unified data repository. The aim is to prescribe personalized medicine based on the thousands of genotype and phenotype parameters for each patient. Discussion and evaluation The model uses a dimension reduction method to reduce training time at an online sliding window setting. We identify the Gleason score as a determining factor for cancer possibility and substantiate our claim through Lilliefors and Kolmogorov–Smirnov test. We present clustering and Random Decision Forest results. The model’s prediction accuracy is compared with standard machine learning algorithms for numeric and categorical fields. Conclusion We propose an ensemble framework of stream and batch data for incremental lifelong learning. The framework successively applies first streaming clustering technique and then Random Decision Forest Regressor/Classifier to isolate anomalous patient data and provides reasoning through root cause analysis by feature correlations with an aim to improve the overall survival rate. While the stream clustering technique creates groups of patient profiles, RDF further drills down into each group for comparison and reasoning for useful actionable insights. The proposed MALA architecture retains the past learned knowledge and transfer to future learning and iteratively becomes more knowledgeable over time.

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

scholarly journals FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

2021 ◽  
Vol 116 ◽  
pp. 30-48
Author(s):  
Bram Steenwinckel ◽  
Dieter De Paepe ◽  
Sander Vanden Hautte ◽  
Pieter Heyvaert ◽  
Mohamed Bentefrit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Data Streams ◽  
Expert Knowledge ◽  
Root Cause Analysis ◽  
Sensor Data ◽  
Cause Analysis ◽  
Root Cause

scholarly journals Artificial Intelligence-Enabled and Period-Aware Forecasting COVID-19 Spread

2021 ◽  
Vol 26 (1) ◽  
pp. 47-57
Author(s):  
Paul Menounga Mbilong ◽  
Asmae Berhich ◽  
Imane Jebli ◽  
Asmae El Kassiri ◽  
Fatima-Zahra Belouadha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Health Management ◽  
Negative Impact ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Time Lags ◽  
Context Sensitive ◽  
Learning Techniques

Coronavirus 2019 (COVID-19) has reached the stage of an international epidemic with a major socioeconomic negative impact. Considering the weakness of the healthy structure and the limited availability of test kits, particularly in emerging countries, predicting the spread of COVID-19 is expected to help decision-makers to improve health management and contribute to alleviating the related risks. In this article, we studied the effectiveness of machine learning techniques using Morocco as a case-study. We studied the performance of six multi-step models derived from both Machine Learning and Deep Learning regards multiple scenarios by combining different time lags and three COVID-19 datasets(periods): confinement, deconfinement, and hybrid datasets. The results prove the efficiency of Deep Learning models and identify the best combinations of these models and the time lags enabling good predictions of new cases. The results also show that the prediction of the spread of COVID-19 is a context sensitive problem.

Root Cause Analysis of Network Failures Using Machine Learning and Summarization Techniques

2017 ◽  
Vol 55 (9) ◽  
pp. 126-131 ◽  
Author(s):  
Jose Manuel Navarro Gonzalez ◽  
Javier Andion Jimenez ◽  
Juan Carlos Duenas Lopez ◽  
Hugo A. Parada G
Keyword(s):  
Machine Learning ◽  
Root Cause Analysis ◽  
Cause Analysis ◽  
Root Cause ◽  
Network Failures
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