Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine

Vibration technique in a machine condition monitoring provides useful reliable information, bringing significant cost benefits to industry. By comparing the signals of a machine running in normal and faulty conditions, detection of defected journal bearings is possible. This paper presents fault diagnosis of a journal bearing based on vibration analysis using three classifiers: Fisher Linear Discriminant (FLD), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The frequency-domain vibration signals of an internal combustion engine with intact and defective main journal bearings were obtained. 30 features were extracted by using statistical and vibration parameters. These features were used as inputs to the classifiers. Two different solution methods - variable K value and RBF kernel width (σ) were applied for FLD, KNN and SVM, respectively, in order to achieve the best accuracy. Finally, performance of the three classifiers was calculated in journal bearing fault diagnosis. The results demonstrated that the performance of SVM was significantly better in comparison to FLD and KNN. Also the results confirmed the potential of this procedure in fault diagnosis of journal bearings

Vibration condition monitoring of planetary gears based on decision level data fusion using Dempster-Shafer theory of evidence

In recent years, due to increasing requirement for reliability of industrial machines, fault diagnosis using data fusion methods has become widely applied. To recognize crucial faults of mechanical systems with high confidence, indubitably decision level fusion techniques are the foremost procedure among other data fusion methods. Therefore, in this paper in order to improve the fault diagnosis accuracy of planetary gearbox, we proposed a representative data fusion approach which exploits Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers and Dempster-Shafer (D-S) evidence theory for classifier fusion. We assumed the SVM and ANN classifiers as fault diagnosis subsystems as well. Then output values of the subsystems were regarded as input values of decision fusion level module. First, vibration signals of a planetary gearbox were captured for four different conditions of gear. Obtained signals were transmitted from time domain to time-frequency domain using wavelet transform. In next step, some statistical features of time-frequency domain signals were extracted which were used as classifiers input. The gained results of every fault diagnosis subsystem were considered as basic probability assignment (BPA) of D-S evidence theory. Classification accuracy for the SVM and ANN subsystems was determined as 80.5 % and 74.6 % respectively. Then, by using the D-S theory rules for classifier fusion, ultimate fault diagnosis accuracy was gained as 94.8 %. Results show that proposed method for vibration condition monitoring of planetary gearbox based on D-S theory provided a much better accuracy. Furthermore, an increase of more than 14 % accuracy demonstrates the strength of D-S theory method in decision fusion level fault diagnosis

Fuzzy logic based classification of faults in mechanical differential

Mechanical differentials are widely used in automotive, agricultural machineries and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause damage, hence condition monitoring of these machines is very important. This paper proposes a data driven model-based condition monitoring scheme that is applied to differential. The scheme is based upon a fuzzy inference system (FIS) in combination with decision trees. To achieve this objective, the acoustic signals from a microphone were captured for the following conditions: Health, bearing fault, worn pinion, broken pinion, worn cranwheel and broken cranwheel for tow working levels of differential (1500 and 3000 r/min). Taken signals were in time domain and for extraction more information was converted from time domain to time-frequency domains using wavelet transformation. Subsequently, statistical features were extracted from signals using descriptive statistic parameters, better features were selected by J48 algorithm and used for developing decision trees. In the next stage, fuzzy logic rules were written using the decision tree and fuzzy inference engines were produced. In order to evaluate the proposed J48-FIS model, the data sets obtained from acoustic signals of the differential were used. The total classification accuracy for 1500 and 3000 r/min conditions were 92.5 % and 95 %, respectively, so the work conducted has demonstrated the potential of used method to classify the fault conditions which are represent in differential

Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA, and SVM)

Se ha estudiado la posible aplicación de una nariz electrónica basada en semiconductores de óxido metálico (e-nariz) como un instrumento que no sea destructivo para el seguimiento del cambio en la producción de volátiles de plátano durante el proceso de maduración. La propuesta de e-nariz no necesita ningún equipo de laboratorio avanzado o caro y resultó ser fiable en la grabación de las diferencias significativas entre las etapas de maduración. El Análisis de Componentes Principales (PCA), Análisis Discriminante Lineal (LDA), Modelado Suave Independiente de las Analogías de Clases (SIMCA) y Máquinas Soporte de Vectores (SVM) son técnicas utilizadas para este propósito. Los resultados mostraron que la dirección de la e-nariz distingue entre las diferentes etapas de maduración. La nariz electrónica fue capaz de detectar una clara diferencia en la huella digital de aroma de plátano cuando se utiliza el análisis de SVM en comparación con PCA o LDA y SIMCA. Utilizando el análisis de SVM, era posible diferenciar y clasificar las diferentes etapas de maduración de plátanos, y este método fue capaz de clasificar el 98,66% del total de muestras en su grupo respectivo. Las capacidades matrices de sensores en la clasificación de etapas de maduración usan el análisis de la carga y la SVM y SIMCA También se ha visto que conduce a desarrollar un sistema de e-nariz específico mediante la aplicación de los sensores más eficaces y a ignorar los sensores redundantesPotential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaning¬ful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.peerReviewe

Discrete wavelet transform and artificial neural network for gearbox fault detection based on acoustic signals

Gearboxes are widely applied in power transmission lines, so their health monitoring has a great impact in industrial applications. In the present study, acoustic signals of Pride gearbox in different conditions, namely, healthy, worn first gear and broken second gear are collected by a microphone. Discrete wavelet transform (DWT) is applied to process the signals. Decomposition is made using Daubichies-5 wavelet with five levels. In order to identify the various conditions of the gearbox, artificial neural network (ANN) is used in decision-making stage. The results indicate that this method allow identification at a 90 % level of efficiency. Therefore, the proposed approach can be reliably applied to gearbox fault detection

Effect of Depth of Plowing and Soil Moisture Content on Reduced Secondary Tillage

This study was carried out to investigate the effect of depth of plowing and soil moisture content dry base on reduce secondary tillage. Split plots have been used for statistical analysis. The Main plot was used for the percentage of soil moisture content and the sub-plot was used for investigation of the effect of depth of plowing. Each treatment was replicated 3 times. For primary tillage and secondary tillage moldboard plow and disk harrow was used, respectively. This research was done in two different land types, one land type had silty clay loam soil and another had loam soil. Based on this research, in silty clay loam soil, the clod mean weight diameter of soil was minimum for each two depth of plowing 15-20 and 25-30 cm with 15-18 % of  soil moisture content, and it was maximum for the same depth but with 10-13 % of soil moisture content. In loam soil, clod mean weight diameter was minimum for 15-20 cm depth of plowing with 18-20 % of soil moisture content and 25-30 cm depth of plowing with 13-18 % of soil moisture content, and it was maximum for two depth of plowing 15-20, 25-30 cm with 10-13 % soil moisture content. Results showed that, if primary tillage was performed in optimum soil moisture content, based on type of soil, we will have suitable seedbed with minimum secondary tillage. It can reduce costs of secondary tillage

Transmitted vibrations to the wrist and arm of a chainsaw operator: The effect of wood cutting process

Introduction: Due to the non-developed mechanization situation, chainsaw is a widely used tool in Iranian forests. This tool can trigger unwelcome disorders (e.g., white finger syndrome) due to the transmitted vibrations from its handle to the body members. Characterizing these vibrations can result in minimizing the intensity of these disorders. This study aims to investigate the effect of different hardwood species (Beech, Hornbeam, and Alder) on the vibrations transmitted to the wrist and arm. Material and Methods: Experiments are conducted during four operations including Beech-, Hornbeam-, and Alder-cross-cutting and without cutting as the control sample. Vibration accelerations in three directions of a local Cartesian coordinate system are measured at three points including chainsaw handle, operator's arm and wrist. Using the time and frequency spectra of vibration accelerations, root mean square (RMS), total vibration acceleration, total vibration transmissibility, and frequency-weighted vibration acceleration are calculated based on ISO 5349 (2001) and ISO 10819 (2013) standards. The calculated parameters were statistically analyzed in SAS. Results: The results showed that variations in wood species could significantly affect the RMS at all three points. The RMS magnitude decreases from handle towards the arm. Interestingly, a significant variation in vibration transmissibility is observed in different frequencies. This study confirms that body organs can damp the high-frequency vibrations better than the low frequency ones. Conclusion: Although the RMS for cutting operations is less than the control sample, frequency-weighted vibration acceleration and consequently the risk of white finger syndrome is higher in cutting regimes. Furthermore, some vibration accelerations (below 40 Hz for wrist and below 25 Hz for arm) are amplified during transmission despite significant damping in total vibrations

A Study on the Challenges Faced By Health Systems in Establishing Risk Management in Selected Hospitals of Tehran University of Medical Sciences

Introduction: This study aimed to identify the challenges of risk management in the context of clinical governance in selected hospitals of Tehran University of Medical Sciences. Materials and Methods:This study was implemented in two phases: qualitative step and quantitative step (survey). The first step was conducted using in-depth interviews and the second was carried out through a survey by questionnaire. Data were collected in hospitals through in-depth interviews with hospitals managers and the experts involved in clinical governance who had been introduced by the hospital manager. All professionals affiliated with clinical governance in Baharloo, Firoozgar, Farabi, Shahid Rajai, Ziaeian, Motahari and Sina hospitals were selected. Results: 35 experts involved in clinical governance were interviewed. According to these experts, the main obstacles in hospital risk management were: Lack of an error reporting culture, exaggerated fear of the consequences, and physicians' lack of interest in this domain. High workloads in this area have led to a reduction in employees' contributions. Conclusion: Establishing clinical governance in health care organizations has had many benefits, such as improving patient care, increased level of patients' satisfaction, establishment of a risk management system, improvement in staff and health-care personnel cooperation, and achieving a more successful organizational management. Appropriate changes in the organizational culture are necessities for the successful establishment of risk management. Human and cultural obstacles that hinder the implementation of risk management in hospitals are evident; thus, major actions are necessary to implement risk management properly in a disciplined manner

Study of the Effects of Planned and Written Training on Anxiety in Neurosurgical Patients

Background & Aim: Anxiety and depression are considered as important complications before surgery. Given the importance of reducing anxiety in patients nominated for surgery, different methods are conducted for this purpose, that patient training is one of these methods, this study was performed with the aim of influence of planned and written education on anxiety in patients scheduled for neurosurgery. Material and Methods: This study was an experimental that 90 neurosurgery patients were randomly divided into 3 groups of planned, written and control training. At the first stage, all patients responded to 40 questions of Spielberger anxiety questionnaire and then intervention was performed in the planned and written training groups. Then assessment of anxiety in patients after training was conducted in three groups. In order to analyze the data, in addition to calculate the mean and standard deviation, independent t-test and ANOVA were used. Results: The results showed that there is no difference between the average anxiety of patients before the training (P> 0.05). But there is difference between patients anxiety in the three groups after the test (P <0.05). And by comparing the mean, it was shown that mean anxiety in two planned and written training groups is decreased after training. Conclusion: Given that nurses play an important role in the investigation and relief of patients anxiety, and compared to other members of the healthcare team spend more time with patients undergoing surgery, the planned training method must therefore be considered to provide effective support to reduce anxiety before surgery. &nbsp