Machine Fault Diagnosis Techniques

This study presents a systematic comparison of three dimensionality reduction methods namely Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and uniform manifold approximation and projection (UMAP) applied to multivariate turbofan engine sensor data from the NASA C-MAPSS benchm…

Minimizing unplanned downtime is critical for maintaining productivity in modern manufacturing. While combining sensor networks with machine learning provides a practical way to detect mechanical failures early, conventional data-driven diagnostics often fail during highly transient stamping operations. This failure s…

In this study, parameter estimation-assisted sensorless control methods are proposed for compressor motors. As sensorless control strategies, rotating high-frequency injection (RHFI), pulsating high-frequency injection (RHFI), and an adaptive-gain sliding mode observer (AG-SMO) are employed. During startup, HFI-based…

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Abstract. Wind and solar energy are two popular alternative energy sources. However, wind turbines are larger and more complex than solar panels. Accordingly, wind turbines are more exposed to environmental factors and therefore more prone to mechanical failures. Our work improves the reliability and efficiency of win…

Defects in the head region of bolts used as fasteners directly affect load transfer, the proper transmission of tightening torque, and the safety of on-site maintenance and repair operations. In mass production lines, monitoring such defects primarily through operator inspection and infrequent sampling is both prone t…

Abstract Turning is a widely used machining process in the industry. However, chatter-induced vibrations limit cutting efficiency and damage tools and workpieces. Despite advances in chatter detection, most methods assume balanced training datasets, which is rarely the case due to the difficulty of collecting chatter…

Early and reliable crack localization in jet turbine blades is important for structural health monitoring in aerospace systems. This study presents an uncertainty-aware Deep Kernel Learning (DKL) framework that integrates a deep residual feature extractor with a Sparse Variational Gaussian Process (SVGP) model for cra…

Rulman arıza teşhisi, endüstriyel döner makinelerin güvenli ve verimli çalışmasının sağlanması açısından kritik bir öneme sahiptir. Bu çalışmada, rulman arızalarının sınıflandırılması amacıyla iki farklı hibrit derin öğrenme modeli olan Evrişimsel Sinir Ağı–Uzun Kısa Süreli Bellek (CNN-LSTM) ve Evrişimsel Sinir Ağı–Ge…

This study investigates and compares Sliding Mode Observer (SMO)-based rotor position and speed estimation methods used in sensorless drive applications of permanent magnet synchronous motors (PMSMs). The estimation performance of these methods in different speed domains is evaluated. Comparative analyses were perform…

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Structural health monitoring systems play a significant role in the early detection of nonlinear damage mechanisms caused by seismic activity and structural fatigue. Damage occurring in modern structures typically leads to complex deviations in time-series data and distortions in the fundamental frequency components t…