Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network

In rotary complex machines, collapse of a component may inexplicably occur usually accompanied by a noise or a disturbance emanating from other sources. Rolling bearings constitute a vital part in many rotational machines and the vibration generated by a faulty bearing easily affects the neighboring components. Continuous monitoring, fault diagnosis and predictive maintenance, is a crucial task to reduce the degree of damage and stopping time for a rotating machine. Analysis of fault-related vibration signal is a usual method for accurate diagnosis. Among the resonant demodulation techniques, a well-known resolution often used for fault diagnosis is envelope analysis. But, usually this method may not be adequate enough to indicate satisfactory results. It may require some auxiliary additional techniques. This study suggests some methods to extract features using envelope analysis accompanied by Hilbert Transform and Fast Fourier Transform. The proposed artificial neural network (ANN) based fault estimation algorithm was verified with experimental tests and promising results. Every test was initiated with a reference ANN architecture to avoid inappropriate classification during the evaluation of fitness value. Later, ANN model was modified using a genetic algorithm providing, an optimal skillful fast-reacting network architecture with improved classification results. (C) 2014 Elsevier Ltd. All rights reserved

Fault Diagnosis of Rolling Bearings Using Data Mining Techniques and Boosting

Rolling bearings are key components in most mechanical facilities; hence, the diagnosis of their faults is very important in predictive maintenance. Up to date, vibration analysis has been widely used for fault diagnosis in practice. However, acoustic analysis is still a novel approach. In this study, acoustic analysis with classification is used for fault diagnosis of rolling bearings. First, Hilbert transform (HT) and power spectral density (PSD) are used to extract features from the original sound signal. Then, decision tree algorithm C5.0, support vector machines (SVMs) and the ensemble method boosting are used to build models to classify the instances for three different classification tasks. Performances of the classifiers are compared w.r.t. accuracy and receiver operating characteristic (ROC) curves. Although C5.0 and SVM show comparable performances, C5.0 with boosting classifier indicates the highest performance and perfectly discriminates normal instances from the faulty ones in each task. The defect sizes to create faults used in this study are notably small compared to previous studies. Moreover, fault diagnosis is done for rolling bearings operating at different loading conditions and speeds. Furthermore, one of the classification tasks incorporates diagnosis of five states including four different faults. Thus, these models, due to their high performance in classifying multiple defect scenarios having different loading conditions and speeds, can be readily implemented and applied to real-life situations to detect and classify even incipient faults of rolling bearings of any rotating machinery

Prognostic factors in patients with aggressive non-Hodgkin's lymphoma without complete response to first-line therapy

This study was conducted to retrospectively identify the prognostic factors that specifically predict survival rates of patients with aggressive non-Hodgkin's lymphoma who did not achieve a complete response (CR) to first-line therapy. Prognostic factors in terms of survival were analyzed in 76 adult patients with non-Hodgkin's lymphoma who had failed to achieve CR to first-line chemotherapy (CT) regimens administered at Istanbul University, Institute of Oncology, between February 1989 and October 1998. A total of 41 patients were female, and median age was 60 y (range, 18-87 y). Twenty-seven patients (35%) had primary refractory disease (stable disease + progressive disease). A partial response (PR) was demonstrated in 49 (65%). In all, 92% had been administered anthracycline on the basis of computed tomography findings. Of 27 patients with primary refractory disease, 20 died because of initial CT toxicity or disease progression. A total of 10 patients with primary refractory disease underwent second-line CT. CR was observed in only I of those patients. Of the 49 patients who had a PR to first-line therapy, 31 died because of disease progression. Of those patients, 14 underwent second-line CT. Four patients were observed to have a CR. Median overall survival (OS) in all patients was established at 15 mo (range, 11-19 mo), and 5-y OS was 25%. On the other hand, median OS in patients with primary refractory disease was 7.6 mo (range, 5.7-9.4 mo) and was observed to be 17.8 mo (range, 9.4-26.1 mo) in patients with a PR. The difference in survival rates between patients with primary refractory disease and those with a PR was significant (P=.005). Although median OS was 18.1 mo (range, 8.4-27.8 mo) in patients with intermediate-grade histology, it was 6.1 mo (range, 1-11.7 mo) in patients with high-grade histology (P=.001). As a result of univariate analysis, significant prognostic factors associated with OS included histologic grade (intermediate/high) (P=.001), response to initial therapy (primary refractory disease/PR) (P=.005), performance status (0-2/2-4) (P=.024), and International Prognostic Index risk groups (low/low intermediate/intermediate-high/high risk) (P=.004). Multivariate analysis revealed that independent prognostic parameters associated with OS included response to initial therapy (P=.009) and histologic grade (P=.001). Although prognosis is rather poor in patients with high histologic grade and primary refractory disease, patients with a PR have a slightly better prognosis