Abstract: A process for creation of an equipment health monitoring (EHM) tool, including defining functional requirements for a proposed EHM tool in a structured hierarchical format. The functional requirements definition is used to generate an outline model for a plurality of functions of the proposed EHM tool according to a model template, wherein the outline model is captured as one or more graphical representations. Each graphical representation includes at least one component representative of a defined EHM functional requirement. Source code for the proposed EHM tool is automatically generated based upon the graphically represented model and then compiled to create a deployable EHM tool from the source code by applying a compilation strategy dependent on an intended mode of operation for said EHM tool. An EHM generation tool and associated data carrier are also recited.

Abstract: 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.

Abstract: 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.