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By implementing a Rapid Application Development (RAD) approach, HAHN Spring Limited can encapsulate IVHM techniques into low-cost prototype software. HAHN Spring Limited can then configure the prototype software for a variety of purposes. Once configured, for instance, the software could be supplied as:

A standalone software system.
A web-based application targeted at the fast on-line visualization and analysis of big datasets.
A library of routines that could be used by your own systems.
A prototype system used for generating requirements to evolve / improve a target IVHM system.
A prototype demonstrating how to exploit existing IVHM resources and bestow on them modern efficient characteristics.

Diagnostic Summary

View a complete diagnostic overview of your system on one screen.

Raw Signal Interrogation

Scroll, zoom, and examine the raw signal in detail and determine whether it has exceeded normal thresholds.

Probability Distribution

Display an estimate of the signal probability distribution, find out how normal the data is, and determine if there are any outlying data points.

Energy Bands

Display the signal energy over frequency bands and determine the significant bands with high energy.

Feature / Condition Indicator Severity

Display any number of selected features and display the severity of their deviations from normalality.

Feature / Condition Indicator Values

Display tabulated values of a selected number of features along with the diagnostic region of each feature and the severity of its deviation from normality.

Diagnostic Condition Severity

Display the severities of the conditions, faults or failure modes that were diagnosed across the acquisitions made during selected operational periods.

Diagnostic Envelopes

Extract from HAHN Spring diagnostic envelopes or Hilbert envelopes features to diagnose faults in machine components such as bearings.

Noisy Data?

Often within a noisy data environment there are hidden objects belonging to distinct classes...

Automonously Identifying Classes of Objects

The novel clustering algorithm can automatically identify classes of objects within a noisy data environment. The objects belonging to minority (small) classes may indicate faulty conditions, extreme operations, abnormal behaviour, etc...

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