Methods and systems for accurate leak detection with minimal false alarms

A method comprising:

• providing a training dataset of a detection signal from a sensor deployed along a pipeline to an artificial neural network, the training dataset comprising training signals representative of anomalies and non-anomalies in respect of the pipeline, wherein the artificial neural network comprises:
• a feature extraction model that extracts from the detection signal a feature map that represents one or more patterns along a temporal dimension represented by a plurality of temporal stamps;
• a temporal processing model that generates from the feature map a plurality of states that capture temporal dependencies, wherein each of the plurality of states corresponds to one of the plurality of temporal stamps, and wherein the temporal dependencies represent an influence on one of the plurality of states by at least one other of the plurality of states;
• and
• a weighting model that assigns a weight to each of the plurality of states to generate weighted representations of the plurality of states using an attention mechanism;
• and
• training, by using the training dataset, the artificial neural network to generate a detection result from the plurality of states using the training dataset.

A system for detecting anomalies in a pipeline, comprising:

• a sensor deployed along the pipeline, configured to generate and transmit a detection signal;
• an artificial neural network configured to receive the detection signal, the artificial neural network comprising:
• a feature extraction model that extracts from the detection signal a feature map that represents one or more patterns along a temporal dimension represented by a plurality of temporal stamps;
• a temporal processing model that generates from the feature map a plurality of states that capture temporal dependencies, wherein each of the plurality of states corresponds to one of the plurality of temporal stamps, and wherein the temporal dependencies represent an influence on one of the plurality of states by at least another of the plurality of states;
• and
• a weighting model that assigns a weight to each of the plurality of states to generate weighted representations of the plurality of states using an attention mechanism,
• wherein the artificial neural network is trained prior to receiving the detection signal to generate a detection result from the plurality of states using a training dataset comprising training signals representative of anomalies and non-anomalies in respect of the pipeline, and wherein a set of data from the training signals is labeled with a leak;
• and
• an output module configured to obtain the detection result from the artificial neural network.

A non-transitory computer-readable medium having instructions stored thereon, the instructions configured when read by a computer to cause the computer to perform a method comprising:

• receiving a detection signal from a sensor deployed along a pipeline;
• inputting the detection signal to an artificial neural network comprising:
• a feature extraction model that extracts from the detection signal a feature map that represents one or more patterns along a temporal dimension represented by a plurality of temporal stamps;
• a temporal processing model that generates from the feature map a plurality of states that capture temporal dependencies, wherein each of the plurality of states corresponds to one of the plurality of temporal stamps, and wherein the temporal dependencies represent an influence on one of the plurality of states by at least another of the plurality of states;
• and
• a weighting model that assigns a weight to each of the plurality of states to generate weighted representations of the plurality of states using an attention mechanism;
• and
• obtaining a detection result from the artificial neural network,
• wherein prior to receiving the detection signal, the artificial neural network has been trained to generate the detection result from the plurality of states using a training dataset comprising training signals representative of anomalies and non-anomalies in respect of the pipeline, and wherein a set of data from the training signals is labeled with a leak.