Automated real-time detection, prediction and prevention of rare failures in industrial system with unlabeled sensor data

A method for a system comprising a plurality of apparatuses providing unlabeled sensor data, the method comprising:

• executing feature extraction on the unlabeled sensor data to generate a plurality of features;
• executing failure detection by processing the plurality of features with a failure detection model to generate failure detection labels, the failure detection model generated from a machine learning framework that applies supervised machine learning on unsupervised machine learning models generated from unsupervised machine learning;
• providing extracted features and the failure detection label to a failure prediction model to generate failure prediction and a sequence of features;
• identifying a root cause of ensemble failures and generating automated remediation recommendations to address the ensemble failures;
• generating alerts from the ensemble failures;
• executing an alert suppression process with cost-sensitive optimization technique to suppress ones of the alerts based on urgency level;
• providing remaining ones of the alerts to one or more operators of the plurality of systems;
• executing processes to control one or more of the plurality of systems based on the remediation recommendations;
• and
• generating the failure prediction model, the generating the failure prediction model comprising:
• extracting features from an optimized feature window from historical sensor data;
• determining an optimized failure window and a lead time window based on failures from the historical sensor data;
• encoding the features with Long Short-Term Memory (LSTM);
• training a LSTM sequence prediction model configured to learn patterns in feature sequences from the feature window to derive failure in the failure window;
• providing die LSTM sequence prediction model as the failure prediction model;
• and
• ensembling failures from detected failures from the failure detection model and predicted failures from the failure prediction model;
• wherein the failure prediction is ensemble failures from detected failures and predicted failures.

A method for a system comprising a plurality of industrial apparatuses providing unlabeled data, the method comprising:

• executing feature extraction on the unlabeled data to generate a plurality of features;
• executing a machine learning framework that transforms unsupervised learning tasks into supervised learning tasks through applying supervised machine learning on unsupervised machine learning models generated from unsupervised machine learning, the executing the machine learning framework comprising:
• executing the unsupervised machine learning to generate the unsupervised machine learning models based on the features;
• executing supervised machine learning on results from each of the unsupervised machine learning models to generate supervised ensembled machine learning models, each of the supervised ensemble machine learning models corresponding to each of the unsupervised machine learning models;
• selecting ones of the unsupervised machine learning models based on an evaluation of the results of the unsupervised machine learning models against predictions by the supervised ensemble machine learning models;
• selecting features based on the evaluation results of the unsupervised learning models;
• and
• converting the selected ones of unsupervised learning models to supervised learning models for facilitating explainable artificial intelligence (AI);
• identifying a root cause of ensemble failures and generating automated remediation recommendations to address the ensemble failures;
• generating alerts from the ensemble failures;
• executing an alert suppression process with cost-sensitive optimization technique to suppress ones of the alerts based on urgency level;
• providing remaining ones of the alerts to one or more operators of the plurality of systems;
• and
• executing processes to control one or more of the plurality of systems based on the remediation recommendations, wherein the executing processes to control comprises at least one of shutting down, rebooting, or triggering warning indicators on the industrial apparatuses based on the predicted failure and the remediation recommendations.

A non-transitory computer readable medium, storing instructions for management of a system comprising a plurality of industrial apparatuses providing unlabeled sensor data, the instructions comprising:

• executing feature extraction on the unlabeled sensor data to generate a plurality of features;
• executing failure detection by processing the plurality of features with a failure detection model to generate failure detection labels, the failure detection model generated from a machine learning framework that applies supervised machine learning on unsupervised machine learning models generated from unsupervised machine learning;
• providing extracted features and the failure detection label to a failure prediction model, the failure prediction model being a Long Short-Term Memory (LSTM) sequence prediction model that receives the extracted features and the failure detection labels and outputs both a predicted failure score indicating likelihood of failure and a predicted sequence of features having a same format as the extracted features from the feature extraction;
• identifying a root cause of ensemble failures and generating automated remediation recommendations to address the ensemble failures;
• generating alerts from the ensemble failures;
• executing an alert suppression process with cost-sensitive optimization technique to suppress ones of the alerts based on urgency level;
• providing remaining ones of the alerts to one or more operators of the plurality of systems;
• and
• executing processes to control one or more of the plurality of systems based on the remediation recommendations,
• wherein the executing processes to control comprises at least one of shutting down, rebooting, or triggering warning indicators on the industrial apparatuses based on the predicted failure and the remediation recommendations.