Mehdi Behzad

This study investigates the performance of a modular flap-type oscillating surge wave energy converter (OSWEC) under irregular wave conditions. A six-module configuration is modeled in the frequency domain using linear potential flow theory and compared with a conventional single-flap configuration to assess the performance improvement from modularization. Hydrodynamic coefficients are obtained using the Boundary Element Method in ANSYS-AQWA. Irregular sea states are represented using the Bretschneider spectrum, and the system response is calculated by solving the multi-degree-of-freedom dynamic equation. The model is validated against benchmark results and shows good agreement in the predicted device response. PTO parameters are varied for each module and sea state to identify the optimal operating point. The results show that PTO impedance tuning improves absorbed power compared with damping-only tuning. Hydrodynamic interactions lead to different optimal PTO parameters across modules and sea states. The central and outer modules also exhibit different responses due to frequency-dependent hydrodynamic coefficients and changes in the spectral distribution of wave energy across frequency components. Overall, impedance tuning improves the performance of the modular configuration compared with the single-flap configuration and provides guidance for the design and control of next-generation ocean renewable energy devices under irregular waves.

Predictive maintenance strategies using oil analysis are highly effective for monitoring mechanical and industrial equipment health. However, oil filters can trap wear particles and make oil condition monitoring less reliable. To address this, analyzing particles in oil filters has emerged as a valuable complementary technique in this research. This study compares diesel engine oil analysis and oil filter analysis using the observational ferrography method (ASTM-D7919-14) on 50 mining equipment samples. Observational ferrography identifies particle type, size, and quantity in filters. Particle density is then classified using the Gaussian Mixture Model (GMM). Results reveal that in 20% of the examined cases, oil analysis shows acceptable results, while filter analysis detects severe engine wear, emphasizing the need for further inspection. This discrepancy underscores the importance of filter analysis in identifying hidden issues. The study recommends combining oil and filter analyses to enhance predictive maintenance accuracy and robustness. Additionally, the economic value of filter analysis is evaluated, offering a cost-benefit assessment of the integrated approach. By demonstrating the complementary nature of these techniques, the research promotes a more holistic predictive maintenance strategy, ultimately improving equipment reliability and operational efficiency.

Intelligent fault diagnosis involves deep learning (DL) algorithms. A hybrid model that integrates both physics-based and data-driven approaches can offer effective health monitoring solutions. In this study, a physics-guided framework is developed, in comparison with classical feedforward neural network (FNN) and convolutional neural network (CNN) models, to be employed in gearbox fault detection. The proposed framework integrates a main vibration-data-driven model, that is called CNN-A trained on either envelope or fast Fourier transform (FFT) signals, with a complementary CNN-B that specifically learns from the gear mesh frequency (GMF) harmonics and their sidebands. The accuracy of fault detection is improved in the framework through a physics-and-data-driven (PDD) parameter fusion mechanism in which the model updates the weights in CNN-A by considering the weights of CNN-B, based on the healthy and faulty classes. The physics-guided DL approach is evaluated using experimental vibration measurements of a gearbox test rig. Experimental data with varying speeds and loads from another gearbox test bench is utilized for validation. It is concluded that the framework is robust under varying operating conditions, achieving higher and more stable accuracy, that is, higher performance reliability, compared to the CNN and FNN models.

Unbalance diagnosis for non-constant speed systems is challenging because the 1X fundamental rotational harmonic magnitude, commonly used as an unbalance indicator, depends on shaft rotational speed. This dependency makes it difficult to separate speed effects from unbalance effects. It has been shown that 1X magnitudes become speed-invariant if they are normalized with respect to the rotational speed in power four for variable-speed wind turbines. However, the applicability of this diagnostic technology to dual-speed machines remains unclear. This study experimentally investigates unbalance diagnosis technologies for dual-speed wind turbines, for which speed-dependent interference is present. Vibration data are collected from the main bearings of two dual-speed wind turbines. Novel residual-based, speed-invariant unbalance diagnostic technology is proposed. The experimental results show consistent statistical distributions of the new diagnosis indicator across low and high-speed operating regimes. These findings confirm the suitability of the proposed technology for unbalance diagnosis for dual-speed rotating machinery.

This paper focuses on the influence of torsional loading on the vibration-based unbalance fault diagnosis technology under variable-speed conditions. The coupled flexural–torsional nonstationary governing equations of motion are obtained and solved numerically. Taking the short-time chirp Fourier transform from the acceleration signal, which is determined from the numerical solutions, the influence of variable loading on the magnitude of the fundamental rotational harmonic—a diagnostic feature for conventional unbalance diagnosis technology—as well as its speed-invariant version for novel unbalance diagnosis technology is assessed. Numerical assessment shows that despite the stationary conditions, where the first rotational harmonic magnitude is independent from the torsional load, the conventional unbalance technology depends on the variable torsional load. However, the novel speed-invariant diagnostic technology is independent of the variable torsional load. The dependency of the conventional unbalance fault diagnosis technology on the variable torsional load and the independency of the novel speed-invariant unbalance diagnostic technology on the variable loading are justified by performing thorough experimental investigations on a variable-speed wind turbine with a permissible level of unbalance.

Rolling element bearings (REBs) are vital in rotating machinery, making fault detection essential for optimal performance and system reliability. This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults using time-domain signals, frequency-domain spectra, and envelope frequency spectrum analysis. The study uses diverse datasets, including laboratory and industrial data under various operating conditions, covering fault types like inner race fault (IRF), outer race fault (ORF), rolling element fault (REF), and healthy (H) states. The main innovation is applying Transfer Learning (TL) with fine-tuning to improve model accuracy in identifying REB conditions by leveraging features learned from diverse datasets. An innovative algorithm is also introduced to identify resonance regions for optimal filter selection in envelope analysis, improving fault-related feature extraction and reducing noise. A preprocessing step that removes speed-related variations further enhances model accuracy by isolating fault features and minimizing the impact of rotational speed. The results show that transfer learning with fine-tuning, combined with the resonance region identification algorithm, significantly enhances fault detection accuracy. The TL-CNN model with envelope signal input achieves the highest accuracy across all scenarios, especially under variable operating conditions, and performs reliably on industrial data.

In sponge iron production plants utilizing the direct reduction method, it is very common to use lobe blowers to transfer process gas. The volumetric efficiency of lobe blowers depends on the clearance between their fixed and moving parts. An increase in this clearance can result in backflow, which means that the flow returns from the high-pressure outlet zone to the low-pressure inlet zone. This research aims to assess the feasibility of using ultrasonic techniques to detect backflow. The sample of this research includes four blowers manufactured by AERZEN company, located in the first and third modules of Khuzestan Steel Company. Data analysis results show that the ultrasonic technique can detect the presence of backflow. Furthermore, verification of the results is conducted through an experiment that includes blocking the blowers’ outlets and measuring the ampere consumption and the output pressure. The findings of verification confirm the results obtained via ultrasonic technique. Furthermore, this research proposes the crest factor ratio feature as a metric for detecting backflow.