Oxford University Innovation Limited

Rolls-royce Corporation

Isis Innovation Limited

Oxford Biosignals Limited

Optimized Systems and Solutions Limited

David Andrew Clifton

Lionel Tarassenko

Dennis M King

Mauricio Christian Villarroel Montoya

Paul Anuzis

Stephen Peter King

Robert William Slater

Patrick Thomson

Farah Colchester

Drew Birrenkott

Heloise Greeff

Ian J Shaylor

Steven P King

Glen Wright Colopy

Marco Pimentel

David J Ault

Marco Andre Figueiredo Pimentel

Ian John Shaylor

Delaram Jarchi

Ian Shaylor

David Ault

Steven King

Dennis King

System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.

System monitor and method of system monitoring to predict a future state of a system

Methods, devices and systems for determining the frequency of a periodic physiological process of a subject, particularly respiration rate or heart rate, are disclosed. In one arrangement plural time windows of physiological data are obtained. Reference features corresponding to modulation modes are identified. Modulations of the reference features are extracted. Quality parameters are obtained by processing the extracted modulations. The quality parameters represent how strongly the extracted modulation exhibits a waveform of the periodic physiological process. The extracted modulations are processed to calculate the frequency of the periodic physiological process of the subject. The frequency of the periodic physiological process may be calculated using only a subset of the extracted modulations, the subset being selected using the quality parameter, or using a combination of the extracted modulations wherein each extracted modulation has a weighting defined by the quality parameter of the extracted modulation.

Method of determining the frequency of a periodic physiological process of a subject, and a device and system for determining the frequency of a periodic physiological process of a subject

Vibration amplitudes are recorded as a function of rotation speed and of frequency and the data is analyzed to estimate a noise floor amplitude threshold for each of a plurality of different speed and frequency sub-ranges. On the basis of training data known to be normal speed-frequency areas which contain significant spectral content in normal operation are deemed 'known significant spectral content', so that during monitoring of new data points which correspond to significant vibration energy at speeds and frequencies different from the known significant spectral content can be deemed 'novel significant spectral content' and form the basis for an alert. The estimation of the noise floor is based on a probabilistic analysis of the data in each speed-frequency area and from this analysis an extreme value distribution expressing the probability that any given sample is noise is obtained.

Method and apparatus for monitoring and analyzing vibrations in rotary machines

A system for monitoring the operation of a surface pump such as a hand-operated water pump or oil pump, which uses an accelerometer mounted to a component of the pump to monitor movement of a pump component, for example the handle, and transmitted via a data connection such as a mobile data communications network to a server. The accelerometer measurements are processed by using a trained model such as a support vector machine to output an indication of the condition of the pump or the level of liquid in the well or borehole served by the pump. The model may be trained using a training data set of sensor measurements associated with liquid level in the well and condition of the pump.

Pump monitoring system and method

An image of a human, animal or machine subject, is analysed to detect regions which include strong periodic intensity variations, such as a photoplethysmogram (PPG) signal in a human or animal, or some periodic vibration in a machine. The image is divided into plural regions of fixed order is fitted to a representative intensity signal for that region. The poles of the fitted autoregressive model are thresholded by magnitude to select only the pole or poles with a magnitude greater than the threshold. The pole magnitude therefore acts as a signal quality index. The dominant pole is representative of the strongest periodic information and the frequency of that spectral component can be derived from the phase angle of the pole. The image may be redisplayed with image attributes, e.g. color-coding, according to the pole magnitude in each region of interest and/or the dominant pole phase angle in each region of interest. In the case of a PPG image signal this can give maps of heart rate and breathing rate.

Method and system for signal analysis

A method of remote monitoring of vital signs by detecting the PPG signal in an image of a subject taken by a video camera such as a webcam. The PPG signal is identified by auto-regressive analysis of ambient light reflected from a region of interest on the subject's skin. Frequency components of the ambient light and aliasing artefacts resulting from the frame rate of the video camera are cancelled by auto-regressive analysis of ambient light reflected from a region of interest not on the subject's skin, e.g. in the background. This reveals the spectral content of the ambient light allowing identification of the subject's PPG signal. Heart rate, oxygen saturation and breathing rate are obtained from the PPG signal. The values can be combined into a wellness index based on a statistical analysis of the values.

Remote monitoring of vital signs

A method and apparatus for data compression, particularly applicable to spectral signals such as Fast Fourier Transforms of vibration data. The data is merged to remove redundant frequencies when recorded at multiple sample rates, thresholded with respect to a noise floor to remove even more redundant data, and then the positions of non-zero signal values, with respect to the noise floor, are recorded in a first dataword and the non-zero signal values themselves are all recorded concatenated to form a second dataword. The compressed data set consists of the first and second datawords, together with the value of the noise floor, maximum original amplitude and the broadband power. In the event of successive data sets having the same or similar locations for non-zero signal values a re-use flag may be set and the locations dataword discarded. Preferably the signal values are non-linearly quantized to further reduce the amount of data.

Method and apparatus for compression and de-compression of spectral data

A method and apparatus for detecting an abnormality in e.g. in operating characteristics or function of a machine, apparatus or system, the method including providing a data sample set comprising n values of a measured physical parameter associated with the apparatus or system generated by repeating a measurement of the physical parameter n times. An extremal measured parameter value is selected from amongst the data sample set, determining a probability of observing the selected parameter value (e.g. of observing a value not exceeding the selected parameter value) by applying the selected parameter value to an extreme value probability distribution function having a location parameter and a scale parameter. The value of the location parameter and the value of the scale parameter are each constructed using an integer value m (e.g. notionally representing the size of a sub-sample data set comprising m of said measured parameter values) in which m is less than n (i.e. m&#x3c;n). A conditional indication is given that the selected parameter value is abnormal according to the value of the probability.

Novelty detection

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Oxford, United Kingdom

David Andrew Clifton