Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the Linear Discriminant Analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a Fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology is evaluated by considering a complete dataset of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.Peer ReviewedPostprint (published version

Three-stage Hybrid Fault Diagnosis for Rolling Bearings with Compressively-sampled data and Subspace Learning Techniques

To avoid the burden of much storage requirements and processing time, this paper proposes a three-stage hybrid method, Compressive Sampling with Correlated Principal and Discriminant Components (CSCPDC), for bearing faults diagnosis based on compressed measurements. In the first stage, Compressive Sampling (CS) is utilised to obtain compressively-sampled signals from raw vibration data. In the second stage, an effective multi-step feature learning algorithm obtains fewer features from correlated principal and discriminant attributes from the compressively-sampled signals, which are then concatenated to increase the performance. In the third stage, with these concatenated features, Multi-class Support Vector Machine (SVM) is used to train, validate, and classify bearing faults. Results show that the proposed method, CS-CPDC, offers high classification accuracies, reduced computation time, and storage requirement, with fewer measurements.National Science Foundation of China; National Science Foundation of Shanghai

Wavelet kernel local fisher discriminant analysis with particle swarm optimization algorithm for bearing defect classification

Feature extraction and dimensionality reduction (DR) are necessary and helpful preprocessing steps for bearing defect classification. Linear local Fisher discriminant analysis (LFDA) has recently been developed as a popular method for feature extraction and DR. However, the linear method tends to give undesired results if the samples between classes are nonlinearly separated in the input space. To enhance the performance of LFDA in bearing defect classification, a new feature extraction and DR algorithm based on wavelet kernel LFDA (WKLFDA) is presented in this paper. Herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. To seek the optimal parameters for WKLFDA, particle swarm optimization (PSO) is used; as a result, a new PSO-WKLFDA algorithm is proposed. The experimental results for the synthetic data and measured vibration bearing data show that the proposed WKLFDA and PSO-WKLFDA outperform other state-of-the-art algorithms

PENERAPAN ALGORITMA K-NEAREST NEIGHBOUR DAN PARTICLE SWARM OPTIMIZATION PADA KLASIFIKASI DAGING BABI, DAGING SAPI, DAN DAGING OPLOSAN

Agama Islam memiliki pedoman hidup yaitu Al-Qur’an dan Hadits, dalam Al-Qur’an Allah SWT memerintahkan makanan yang halal lagi baik pada Q.S. Al-Baqarah ayat 168. Salah satu makanan yang diharamkan yaitu daging babi. Sehingga penelitian ini dilakukan agar umat Islam dapat membedakan daging babi dan daging sapi karena banyaknya pedagang mencampurkan daging babi dan daging sapi tersebut. Jumlah data pada penelitian ini sebanyak 200 data dengan ukuran piksel 300 x 300, dan ISO maksimum 200. Penelitian ini menggunakan parameter bobot inersia (w) yaitu 0,6 , learning rate partikel (c1) yaitu 1 dan antarpartikel (c2) yaitu 1,2. Proses pengenalan citra meliputi ekstraksi ciri warna HSV, ekstraksi ciri GLCM, KNN (kfold cross validation) sebagai klasifikasi dan PSO sebagai optimasi. Pada penelitian ini menghasilkan nilai akurasi KNN berbasis PSO sebesar 55.6% pada posisi