Evidence map›Paper›PMID 41977970›Full record

ArticleSensors (Basel, Switzerland)2026

Novel Fault Diagnosis Technology Based on Integrated Spectral Kurtosis for Gearboxes.

Len Gelman, Rami Kerrouche, Abdulmumeen Onimisi Abdullahi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Len GelmanDepartment of Engineering, School of Computing and Engineering, The University of Huddersfield, Huddersfield HD1 3DH, UK.ORCID 0000-0001-5464-6227
Rami KerroucheDepartment of Engineering, School of Computing and Engineering, The University of Huddersfield, Huddersfield HD1 3DH, UK.ORCID 0000-0003-4808-5106
Abdulmumeen Onimisi AbdullahiDepartment of Engineering, School of Computing and Engineering, The University of Huddersfield, Huddersfield HD1 3DH, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a novel integrated spectral kurtosis (ISK) technology, which is a new conceptualization for fault diagnosis, and compares it with conventional spectral kurtosis technology. The vibration signals from a gearbox are processed by time synchronous averaging (TSA) and analysed using the spectral kurtosis (SK). The ISK feature is estimated across the entire frequency domain, while the envelope is obtained through SK-based filtering and a Hilbert demodulation. The ISK technology demonstrates the ability to distinguish between healthy and defected gearbox cases, achieving a total probability of correct diagnosis (TPCD) of 91.5% for pinions and 96.1% for gears, whereas the SK-based squared envelope technology provides a limited diagnosis effectiveness, with a maximum TPCD of 80%. The motor current signals are also analysed through harmonic amplitude tracking within the current spectrum. A comparison of the ISK and motor current technologies is also made, showing that the motor current technology reaches a maximum of 90% TPCD for gears, which remains lower than the TPCD for the ISK technology.

Indexed as

condition monitoringfault diagnosisgearboxmotor current signature analysissignal processingspectral kurtosisvibration

Identifiers

PMID41977970
PMCPMC13075318

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.