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Öğe A new approach for remaining useful life prediction of bearings using 1D-ternary patterns with LSTM(Springer Heidelberg, 2023) Akcan, Eyyup; Kaya, YilmazBearings frequently experience malfunctions in mechanical systems, directly impacting system performance. Accurate prediction of bearing failures is crucial for maintenance planning and preventing unexpected system breakdowns. Data-driven prognostic techniques are commonly employed to estimate the remaining useful life (RUL) of high-speed bearings. RUL prediction relies on establishing the fundamental relationship between bearing degradation and its current health status, with the accuracy depending on effective feature extraction from the bearing data. In this study, a novel approach is proposed for the RUL prediction of bearings. The 1D-TP method is applied to vibration signals, resulting in two feature vectors, LOWER and UPPER, which are then utilized in combination with LSTM for RUL prediction. The proposed approach is evaluated using a dataset from the PRONOSTIA platform, and performance metrics including MAE, RMSE, SMAPE, RA, and Score are determined. The results demonstrate that the 1D-TP + LSTM method successfully predicts the remaining life of bearings. Accurate RUL assessment and reliability analysis aid personnel in making informed maintenance decisions, preventing losses from mechanical system damage, improving production safety, and safeguarding the mechanical system from harm.Öğe An Effective Method for Detection of Demagnetization Fault in Axial Flux Coreless PMSG With Texture-Based Analysis(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Minaz, Mehmet Recep; Akcan, EyyupDue to its high power densities and compact dimensions, the axial flux coreless permanent magnet synchronous generator (PMSG) is used in a wide range of areas such as wind turbines and electric vehicles. It is extremely important to detect magnetization faults that occur in these generators. The occurrence of such faults in these machines with a wide range of areas of use affects their operation negatively. In this study, an effective method has been proposed to detect the demagnetization fault occurring in axial flux coreless PMSGs. The relevant method proposes an effective texture analysis-based feature extraction method, which is an original method in contrast to conventional methods used in the literature. It has been revealed that it is a method that can be used instead of conventional methods such as time-frequency analysis, frequency spectrum analysis, and motor current signature analysis (MCSA) methods. Using the finite element method, current and voltage signals were taken from the healthy and axial flux coreless PMSG with 3% and 6% demagnetization fault. Besides, these signals were retaken at different speeds and loads. After the signals were converted into images, using the features obtained from the images with LBP, fault diagnosis processes were carried out with Knn. It was tested both at different fault rates and under different load and speed conditions to test whether the proposed method worked properly. The success rate of this method was observed as 97.16% and 100%. With the proposed method, it has been revealed that the demagnetization fault can be detected in axial flux coreless PMSGs.Öğe An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method(Elsevier, 2024) Kaya, Yilmaz; Kuncan, Melih; Akcan, Eyyup; Kaplan, KaplanBearings serve as fundamental components in the transmission of motion for rotating machinery. The occurrence of mechanical wear and subsequent bearing failures within these rotating systems can lead to diminished operational efficiency and, if left unaddressed, may result in the complete cessation of the system's function. Hence, there exists a critical need for effective monitoring methodologies aimed at accurately detecting faults in such systems, preferably in their nascent stages. This study presents a novel approach to fault diagnosis leveraging vibration data obtained from bearings. Initially, a feature extraction technique is devised, which incorporates localized signal variations. Subsequently, these features, extracted via MM-1D-LBP, are utilized in conjunction with a hybrid deep learning network based on Long Short-Term Memory (LSTM) and onedimensional Convolutional Neural Network (1D-CNN) architectures for diagnostic purposes. To assess the efficacy of the proposed methodology, experiments were conducted on two distinct datasets acquired from realworld bearing assemblies. In the first dataset, the aim was to predict various failure types (Inner Ring, Outer Ring, Ball). In the second dataset, the objective was to estimate defect sizes using bearing vibration signals corresponding to defects of different dimensions (0.15 cm, 0.5 cm, 0.9 cm) under consistent operating conditions. Remarkably high success rates of 99.31 % and 99.65 % were achieved for the two datasets, respectively, thus underscoring the efficacy of the proposed MM-1D-LBP+1D-CNN-LSTM approach. These findings not only demonstrate the feasibility of the proposed method for fault diagnosis in bearing systems but also suggest its potential applicability across diverse signal categories. Ultimately, this research contributes to advancing the state-of-the-art in fault diagnosis methodologies for rotating machinery, offering enhanced accuracy and early detection capabilities.