Spectral Fault Receptive Fields (SFRFs) constitute a computational model of health perception for rotating machinery, inspired by the center-surround receptive fields observed in biological sensory systems.

Center and surround spectral masks define structured frequency supports that are combined through receptive-field response functions and contrast mappings, producing low-dimensional descriptors that capture degradation trends while remaining interpretable by construction. By decoupling spectral selectivity, response aggregation, and contrast computation, SFRFs support flexible perceptual channels tailored to condition monitoring, diagnosis, and prognosis tasks.

This MATLAB toolbox provides a reference implementation of the SFRF conceptual model and its generalizations, enabling reproducible research and development of health monitoring pipelines for rotating machinery, with particular emphasis on rolling-element bearings.

About this release (v1.1.0)

This record archives the exact source code snapshot and MATLAB toolbox package corresponding to the official v1.1.0 release of the SFRFs Toolbox.

Compared to the initial public release, this version extends the toolbox with additional schema abstractions, contrast-mapping utilities, visualization components, and an updated and expanded API documentation.

The archive includes:

• the packaged MATLAB toolbox (SFRFsToolbox_v1.1.0.mltbx)

• the complete source code snapshot corresponding to the v1.1.0 in GitHub release (matlab-sfrfs-1.1.0.zip)

• architectural diagrams, documentation, and development notes (contained in the devel and doc folders of the source archive)

Full documentation and the rendered API reference are available via the GitHub project repository.

Funding

This work was carried out within the framework of the ARCHIMEDES project, supported by the Chips Joint Undertaking under Grant Agreement No. 101112295, with co-funding from National Authorities. Additional support was provided by the Austrian Research Promotion Agency (FFG) under Grant Agreement No. FO999899377.