Tim Herrmann

2026

1. Mannone, Giuseppe; Herrmann, Tim; Dazer, Martin: Gated Residual End-of-Life Prediction for Variable-Prefix RUL in the PHME2026 Data Challenge. In: PHM Society European Conference 9(1), S. 1–10, Oslo, Norway, 2026. — ISBN 978-1-936263-44-8 — (peer-review)
   • Zusammenfassung
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   Zusammenfassung

   This paper presents a gated residual end-of-life (EOL) method for variable-prefix remaining useful life (RUL) prediction in the PHME 2026 Data Challenge. The task is to estimate the lifetime of partially observed subway-door actuation runs under hidden operating conditions. We frame the problem as EOL estimation: the model first predicts the cycle at which failure occurs and then derives RUL by subtracting the current cycle index. This gives prefixes of different lengths a common lifetime scale. The approach uses engineered prefix features that summarize electrical load, position behavior, shock events, trends, and source-model predictions. Several source models provide an initial EOL prior, while disagreement among them serves as an uncertainty signal. Rather than replacing this prior with one unconstrained predictor, the method refines it through bounded residual corrections. To keep the prediction stable, the method first forms a conservative anchor and calibrates it to the inference setting using released test measurement features. Two bounded specialists then propose residual corrections: one from similar training prefixes and one from shock, position, and current signal evidence. When these specialists disagree, a clipped gate controls how much each correction affects the final EOL estimate. Local diagnostics indicate where the source prior is sufficient, where corrections add value, and where difficult prefixes remain uncertain.

   DOI

   https://doi.org/10.36001/phme.2026.v9i1.4911
2. Herrmann, Tim; Dazer, Martin: Cyclostationary Vibration Analysis and Advanced Signal Pre-Processing for Bearing Fault Detection in Electric Drive Units. In: Proceedings of the European Safety and Reliability Conference (ESREL 2026), Braga, Portugal, 2026
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   Zusammenfassung

   Rolling bearings are critical components for the reliable operation of rotating machinery. As raceway damage is one of the most common failure modes, continuous condition monitoring is essential for its early detection. This enables a predictive maintenance strategy, ensuring cost-effective and optimized repairs. To enhance the model’s fidelity to real-world conditions, a complete automotive electric drive unit, including its various excitations is analyzed using a multibody simulation. Since the cyclostationary nature of raceway faults prevents unambiguous detection using conventional frequency analysis, the envelope spectrum is the established diagnostic standard. However, its effectiveness is severely limited in integrated electric drives. Due to the high-energy deterministic background noise generated by the transmission, the weak stochastic fault signatures are frequently masked, preventing reliable detection even in the envelope domain. To overcome this limitation, this paper evaluates advanced signal preprocessing techniques designed to enhance the signal-to-noise ratio prior to demodulation. Among the investigated methods, cepstrum pre-whitening (CPW) has proven particularly effective in suppressing deterministic excitations, thereby removing superimposed gear mesh excitations from the signal. This significantly improves early fault detection capability. When combined with minimum entropy deconvolution (MED), the impulsiveness of the signal is further optimized, allowing the damage characteristics to be highlighted even more distinctly.

   Link

   https://cmsweb.com.sg/rps2prod/esrel2026/epro/html/esrel26-p26302.html

   DOI

   10.3850/ESREL2026061419_esrel26-p26302-cd