Abstract: A method of detecting a rotor bar fault of an electrical machine, said method comprising, acquiring a set of online signals from the electrical machine over a period, said set of online signals comprising at least one vibration signal, and a summed signal of magnetic flux signals, each magnetic flux signal obtained from a respective flux sensor positioned on an external surface of the electrical machine; extracting an online anomaly indicator value from the set of online signals; comparing the online anomaly indicator value with a baseline anomaly indicator value of a corresponding loading condition; and determining presence of the broken or cracked rotor when the online anomaly indicator value deviates from the baseline anomaly indicator value by a threshold.

Abstract: The present disclosure relates to at least one method for ascertaining at least one attribute of pressure vessel wall. A method comprises: while a pressure vessel, which comprises a wall having an outer surface and an inner surface, is operating: ascertaining a current wavelength shift of a first fiber Bragg grating (FBG) sensor disposed at a first outer surface location on the pressure vessel; and ascertaining a current thickness of the wall at the first outer surface location by: computing a vector based on a time series of the current wavelength shift, or a derivative of the time series, or a derivative of the current wavelength shift; providing the vector to a predetermined regression model; and using the regression model, ascertaining the current thickness of the wall.

Abstract: A method and a system of detecting winding fault during online operation of an electrical machine, said method comprising, acquiring a set of signals from the machine over a period, said set of signals comprising two or more magnetic flux signals, each flux signal obtained from a respective flux sensor positioned on the external surface of the machine; extracting an internal winding fault indicator from the set of signals; comparing the internal winding fault indicator with a baseline indicator; and determining the internal winding fault when the internal winding fault indicator deviates from the baseline indicator by a threshold.

Abstract: A system for predicting an effluent parameter associated with a wastewater treatment process including a predictor module configured to receive a first input dataset comprising a plurality of wastewater inflow parameters to predict a biodegradable type of effluent wastewater; a mechanistic simulator configured to receive the biodegradable type of effluent wastewater and the plurality of wastewater inflow parameters as a second input dataset to predict the effluent parameter.

Abstract: A system for wastewater treatment process control comprising a set of measuring means arranged to obtain a dataset, the dataset comprises a plurality of process variables related to a parameter of the wastewater treatment process; a prediction module arranged to receive the dataset and predict the parameter of wastewater treatment process based on a soft sensor; a troubleshooting module arranged to compare the predicted parameter with a predetermined criterion; wherein if the predicted parameter does not satisfy the predetermined criterion; the troubleshooting module is operable to identify at least one process variable from the plurality of process variables which causes the predicted parameter not to satisfy the predetermined criterion and determine whether the identified at least one process variable from the plurality of process variables is controllable. An optimisation module for use in a wastewater treatment system is also disclosed.