Detection of structural anomalies in a pipeline network

A system for processing a data signal obtained from a sensor sensing a dynamic signal including signal components caused by vibro-acoustic energy generated by fluid flow at a location within an operational mains water distribution pipeline network to detect an indication of a structural anomaly event, the system including:

• a memory;
• a set of computer readable instructions stored in the memory;
• a processor coupled to the memory, the processor configured to execute the set of computer readable instructions and use a machine learning model trained to detect an indication of the structural anomaly event proximal the location and classify the structural anomaly event as either an occurrence and/or further development of a circumferential crack or a longitudinal crack, to:
• periodically sample the data signal at a predetermined time interval to obtain a set of sampled data segments(S) for a time period;
• process the data segments to determine power spectral density values for each data-segment;
• process at least the determined power spectral density values using the neural network;
• and
• generate an alert in response to the machine learning model detecting an indication of a structural anomaly event, the alert identifying the location and classification of the structural anomaly event,
• wherein the machine learning model is trained using temporal changes in one or more frequency domain features derived from power spectrum density values for previous time periods to distinguish between circumferential and longitudinal cracks.

An apparatus for processing a data signal obtained from a sensor sensing a dynamic signal including signal components caused by vibro-acoustic energy generated by fluid flow at a location within an operational mains water distribution pipeline network to detect an indication of a structural anomaly event, the apparatus including:

• means for periodically sampling the data signal at a predetermined time interval to obtain a set of sampled data segments (S) for a time period;
• means for processing the data segments to determine power spectral density values for each data segment;
• means for processing at least the determined power spectral density values using a machine learning model trained to detect an indication of the structural anomaly event proximal the location and classify the structural anomaly event as either an occurrence and/or further development of a circumferential crack or a longitudinal crack, the machine learning model trained using temporal changes in one or more frequency domain features derived from power spectrum density values for previous time periods to distinguish between circumferential and longitudinal cracks;
• and
• means for generating an alert in response to the machine learning model detecting the indication of a structural anomaly event, the alert identifying the location and classification of the structural anomaly event.

A non-transitory computer-readable storage device storing instructions that, when executed by a processor, cause the processor to perform operations comprising:

• obtaining a data signal from a sensor sensing a dynamic signal including signal components caused by vibro-acoustic energy generated by to fluid flow at a location within an operational pipeline network;
• processing the data signal to determine power spectral density values for one or more frequency bands of the data signal;
• processing at least the determined power spectral density values using a machine learning model trained to detect an indication of the structural anomaly event proximal the location and classify the structural anomaly event as either an occurrence and/or further development of a circumferential crack or a longitudinal crack, the machine learning model trained using temporal changes in one or more frequency domain features derived from power spectrum density values for previous time periods to distinguish between circumferential and longitudinal cracks;
• and
• generating an alert in response to the machine learning model detecting an indication of a structural anomaly event, the alert identifying the location and classification of the structural anomaly event.