Waldemar Bauer

Jerzy Baranowski, Waldemar Bauer

This paper proposes a scenario-based framework for predictive maintenance scheduling under uncertainty in a finite planning horizon. The considered setting involves multiple assets for which maintenance decisions are informed by three heterogeneous sources of information: calendar-based overhaul intervals, usage-based limits driven by uncertain future operating cycles, and condition-monitoring outputs represented through remaining useful life (RUL) estimates with uncertainty. While these elements have been studied extensively in the maintenance literature, they are often treated separately or only partially integrated. In contrast, the proposed formulation evaluates complete maintenance schedules under simulated future scenarios and compares them using expected-cost and tail-risk criteria. The contribution is primarily conceptual and methodological: we define a unified finite-horizon decision framework that combines calendar-, usage-, and prognostics-based information within a common scheduling problem. A small synthetic computational example is used as a proof of concept. The results show that integrated scenario-based policies can substantially outperform simpler single-trigger rules, while the difference between risk-neutral and risk-aware integrated policies remains modest under the present calibration.

Waldemar Bauer, Marta Zagorowska, Jerzy Baranowski

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.

Waldemar Bauer, Jerzy Baranowski

This paper proposes a low-data predictive maintenance framework for automatic doors and elevators in a railway station building. The method is intended for assets without direct condition monitoring, where only aggregate passenger traffic information and expert knowledge about movement patterns are available. Passenger flows are modeled on a reduced station graph using a Bayesian formulation with uncertain totals and routing shares. The inferred flows are converted into approximate operating-cycle loads for doors and elevators through simple stochastic proxy relations. These loads are combined with uncertain age- and cycle-based maintenance thresholds to estimate the probability that predefined maintenance conditions have been reached. A cost-aware scheduling model is then used to align maintenance activities while accounting for service costs, disruption, delay penalties, and grouping opportunities within each asset class. The framework is illustrated on a simulated case study reflecting a real station layout. The results show that proxy operational data can support maintenance scheduling with low incremental implementation cost and can improve alignment relative to a calendar-based policy.

Waldemar Bauer, Jerzy Baranowski

This paper compares Bayesian and classical feature ranking methods for interpretable fault diagnosis of brushless DC (BLDC) motors. Two Bayesian approaches, spike-and-slab and ARD logistic ranking, are evaluated against three classical baselines on a public BLDC benchmark in binary and multiclass settings using current-based, rotational-speed-based, and combined feature sets. The strongest overall results are obtained for the combined representation. In binary classification, ReliefF achieves the highest balanced accuracy of 0.923, while ARD logistic and spike-and-slab remain very close at 0.919 and 0.920 with much smaller subsets ($k=5$). In multiclass classification, ARD logistic performs best for the combined variant with balanced accuracy 0.914, followed closely by LASSO (0.913) and spike-and-slab (0.912). The results show that Bayesian ranking is particularly competitive for current-only and combined descriptors, while ReliefF remains especially effective for speed-based ranking. Because the benchmark consists of short segmented observations from a limited number of experimental conditions, the findings are interpreted primarily as benchmark-specific evidence rather than strong claims of fault generalization.

Waldemar Bauer, Jerzy Baranowski

Fault detection in electric motors is a critical challenge in various industries, where failures can result in significant operational disruptions. This study investigates the use of Recurrent Neural Networks (RNNs) and Bayesian Neural Networks (BNNs) for diagnosing motor damage using acoustic signal analysis. A novel approach is proposed, leveraging frequency domain representation of sound signals for enhanced diagnostic accuracy. The architectures of both RNNs and BNNs are designed and evaluated on real-world acoustic data collected from household appliances using smartphones. Experimental results demonstrate that BNNs provide superior fault detection performance, particularly for imbalanced datasets, offering more robust and interpretable predictions compared to traditional methods. The findings suggest that BNNs, with their ability to incorporate uncertainty, are well-suited for industrial diagnostic applications. Further analysis and benchmarks are suggested to explore resource efficiency and classification capabilities of these architectures.