## 1 Introduction  
During operation, diesel engine crankshafts experience significant torque fluctuations due to factors including periodic variations in cylinder pressure and the inherent characteristics of the crank-slider mechanism [1, 2, 3]. These fluctuations induce torsional vibrations, a critical dynamic phenomenon that directly impacts the reliability, durability, and operational safety of the entire powertrain, particularly in demanding applications like marine propulsion [4, 5]. Consequently, the accurate analysis of these vibrations is vital not only for design validation but also for the development of advanced intelligent systems for condition monitoring and fault diagnosis [6, 7]. The predominant method for measuring crankshaft torsional vibration involves the analysis of instantaneous angular speed (IAS) [8, 9]. This technique typically employs a touched disc, such as the engine's flywheel ring gear, in conjunction with a proximate sensor (e.g., magneto-electric, Hall-effect, or optical) to generate a pulse train corresponding to passing gear teeth [10, 11]. By precisely processing the timing of these pulses, the engine's IAS can be calculated, providing a rich data source for identifying torsional vibration characteristics and diagnosing malfunctions, such as injector faults or combustion anomalies [12]. The efficacy of this entire process hinges on the fidelity of the acquired signal and the robustness of the analytical tools, like spectral analysis, used to interpret it [13]. Therefore, significant research has focused on developing high-precision angular sensors and signal acquisition hardware capable of operating reliably in harsh engine environments [14; 15]. Despite the method's widespread use, its accuracy is severely compromised by the complex vibrational environment of an operating diesel engine. Parasitic vibrations, including crankshaft bending and lateral whirl, are inevitably transmitted to the measurement location [16; 17]. These vibrations cause dynamic runout--a combination of radial and axial displacement--of the toothed disc relative to the stationary sensor. This issue is a primary and well-documented source of error in torsional vibration measurement [18; 19]. This runout induces instantaneous changes in the air gap between the gear teeth and the sensor, which contaminates the output signal with modulation errors that are unrelated to the actual torsional dynamics, thereby hindering accurate analysis and diagnosis [20; 21]. In severe cases, particularly during transient operation, this distortion can lead to missed pulses or spurious data points, rendering the derived speed data unusable [22]. To mitigate the interference caused by toothed disc runout, several strategies have been proposed. One direct approach involves the mechanical reinforcement of the sensor bracket and other components to physically suppress the relative vibration [23]. However, this method often requires significant modifications and may not be feasible in space-constrained applications. The usage of digital filters to remove noise is common, but this approach carries the significant risk of distorting the signal of interest or inadvertently removing valuable features within certain frequency bands, which can lead to incomplete or misleading results [24]. The limitations of these existing methods highlight the need for a more robust approach to eliminate runout-induced errors at the source. While previous studies have addressed runout through mechanical reinforcement, vibration-based level adjustment, or general digital filtering, these methods suffer from limitations such as invasive installation, demanding prerequisites, or potential signal distortion. This paper makes a distinct contribution by proposing a composite signal processing strategy that tackles the runout problem at both micro and macro levels. The key novelties are as follows:  
1.