Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Diseño de un sistema fotovoltaico aislado para mejorar el suministro eléctrico de la institución educativa Señor Cautivo del centro poblado Angash, Jaén, 2022.

En esta tesis se ha propuesto el dimensionamiento de un sistema fotovoltaico aislado de la red, donde el objetivo general obedece a diseñar un sistema fotovoltaico aislado para el suministro eléctrico de la Institución Educativa Señor Cautivo, del Centro Poblado Angash, Provincia de Jaén, Departamento de Cajamarca. La metodología que se utilizó es una investigación aplicada de diseño preexperimental. Asi mismo, hoy en día la generación de electricidad a partir de energías renovables está siendo de mucha importancia, porque contribuye con el cuidado del medio ambiente dejando de emitir gases contaminantes, es por eso que nuestro tema se enfoca en la generación de electricidad a partir de un sistema fotovoltaico aislado que generará energía limpia. Los datos de radiación solar fueron tomadas de data de la NASA siendo este de 3.73 HSP en el mes de febrero. Para cubrir la demanda de energía eléctrica de 11.92 kWh/dia, se concluyó que se necesitarán 28 módulos fotovoltaicos de 200 Wp, además contará con 06 reguladores de 100 A, Un banco de 28 baterias de 12 V y 947,22 Ah/día, y por último un inversor de 15 kW. El presupuesto es de S/. 79,505.62; con un VAN de S/. 18,707.39 y un TIR de 15.79%, concluyendo que el proyecto si es factible

SHAFT ALIGNMENT MEASUREMENT SYSTEM DEVELOPED FOR INDUSTRIAL APPLICATIONS

In the industry, the shaft misalignment is considered a common fault in rotating machines. Inadequate alignment of rotating shafts through couplings often lead to severe vibration complications with premature failure of machines parts. It is, without uncertainty, the greatest loss of profits allocated to misalignment, resulting limited production, increasing energy cost, increasing downtime and premature breakdown of the equipment. It´s of a big paramount to optimize the rotating machines efficiency by an appropriate alignment technique. Therefore, from aforementioned, the main objective of this research work is to develop a low-cost, with high precision shaft alignment measurement system for industrial applications. The developed prototype was based on an inductive sensor system, which is a non-contacting and electronic dial indicator equipment. It was used an Arduino Uno for the data acquisition procedure and Matlab® for the data analysis processes. The performance and the effectiveness of the proposed measurement system were verified by an experimental validation procedure. Finally, the research approach was successfully accomplished, by developing a shaft alignment system with ultra-low cost with high degree of accuracy. The overall average standard deviation of the experimental data set was about 0.02 mm, which is under the standard recommended values for alignment

VIBRATION BASED RECONSTRUCTION OF THE CYLINDER PRESSURE IN DIESEL ENGINES BY USING NEURAL NETWORKS

The cylinder pressure curve is a very important parameter for detection of malfunctioning of combustion process in diesel engines. It provides a considerable amount of information about the performance of the engine. The traditional method to get the cylinder pressure curve is to use a cylinder pressure transducer, which is inserted in the cylinder head of the engine. This method is both expensive because of the high cost of the transducer and lifetime limited due to the harsh working environment. Therefore, there is an increasing need of a new non-intrusive method, which can be applied for the reconstruction of the cylinder pressure. The main objective of this paper is to perform the reconstruction of the cylinder pressure curve from vibration measurements by using the Neural Network Method (NNM). The cylinder pressure data obtained with transducers on operating engines was simultaneously recorded with vibration data obtained with external accelerometers at Scania Acoustic Laboratory in Stockholm (Sweden). The measured data were used to train the Neural Networks (NN), thereafter a new data set of vibration signals was enter to the NNs to get the reconstructed cylinder pressure signal. Finally, the results showed high accuracy and precision. The standard deviation of the average maximum cylinder pressures (PMax) varied between 0.03 and 1.01 percent, much lower than those obtained with other methods i.e. Cepstrum Method and Multivariate Data Analysis (MVDA). The final goal to use the NNM for optimization of the combustion process and engine diagnostics was fulfilled

A REVIEW OF VIBRATION MACHINE DIAGNOSTICS BY USING ARTIFICIAL INTELLIGENCE METHODS

In the industry, gears and rolling bearings failures are one of the foremost causes of breakdown in rotating machines, reducing availability time of the production and resulting in costly systems downtime. Therefore, there are growing demands for vibration condition based monitoring of gears and bearings, and any method in order to improve the effectiveness, reliability, and accuracy of the bearing faults diagnosis ought to be evaluated. In order to perform machine diagnosis efficiently, researchers have extensively investigated different advanced digital signal processing techniques and artificial intelligence methods to accurately extract fault characteristics from vibration signals. The main goal of this article is to present the state-of-the-art development in vibration analysis for machine diagnosis based on artificial intelligence methods

Development of a Low-Cost Vibration Measurement System for Industrial Applications

Vibration-Based Condition Monitoring (VBCM) provides essential data to perform Condition-Based Maintenance for efficient, optimal, reliable, and safe industrial machinery operation. However, equipment required to perform VBCM is often relatively expensive. In this paper, a low-cost vibration measurement system based on a microcontroller platform is presented. The FRDM K64F development board was selected as the most suitable for fulfilling the system requirements. The industrial environment is highly contaminated by noise (electromagnetic, combustion, airborne, sound borne, and mechanical noise). Developing a proper antialiasing filter to reduce industrial noise is a real challenge. In order to validate the developed system, evaluations of frequency response and phase noise were carried out. Additionally, vibration measurements were recorded in the industry under different running conditions and machine configurations. Data were collected simultaneously using a standard reference system and the low-cost vibration measurement system. Results were processed using Fast Fourier Transform and Welch’s method. Finally, a low-cost vibration measurement system was successfully created. The validation process demonstrates the robustness, reliability, and accuracy of this research approach. Results confirm a correlation between signal frequency spectrum obtained using both measurement systems. We also introduce new guidelines for practical data storage, communications, and validation process for vibration measurements

A COMPARATIVE OF CURVE-FITTING ALGORITHMS FOR THE EXTRACTION OF MODAL PARAMETERS FROM RESPONSE MEASUREMENTS

The main objective of this paper is to perform a comparison of several curve-fitting methods for extraction of the modal parameters from response vibration measurements, and in particular the best damping estimates. Measurements were carried out on a steel beam to which a constrained layer had been added to make the damping more similar to that of vehicle structural components. Two shakers with different excitation signals, a periodic impulsive and a random signal, respectively, excited the structure, but after separation, only the random part was analysed for the results of this paper. This study compares a number of common curve fitting methods, viz: The Rational Fraction Polynomial Method, the Complex Exponential Method, the Complex Cepstrum Method, the Hilbert Envelope Method and the Ibrahim Time Domain method. The most accurate results for detection of the damping and natural frequencies were obtained by using the Ibrahim Time Domain Method, with the Rational Fraction Polynomial method very similar. The Hilbert Envelope method gave comparable damping estimates. The Cepstrum and Complex Exponential methods gave reasonable results for the frequencies, but not for the damping

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%

Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram

Local faults of rotating machinery usually result in repetitive transients whose impulsiveness or cyclostationarity can be employed as faulty signatures. However, to simultaneously accommodate the impulsiveness and the cyclostationarity is a challenging task for rotating machinery diagnostics. Inspired by recently-reported infogram that is sensitive to either the impulsiveness or the cyclostationarity using spectral negentropy defined in time domain or frequency domain, a multiscale clustering grey infogram (MCGI) is proposed by combining both negentropies in a grey fashion using multiscale clustering. Fourier spectrum of the vibration signal is decomposed into multiple scales with different initial resolutions. In each scale, fine segments are grouped using hierarchical clustering. Meanwhile, both time-domain and frequency-domain spectral negentropies are taken into account to guide the clustering through grey evaluation of both negentropies. Numerical simulations and experimental tests are carried out for validating the proposed MCGI. For comparison, peer methods are applied to challenge different noises and interferences. The results show that, thanks to the multiscale clustering of the spectrum and the grey evaluation of both negentropies, the present MCGI is robust in extracting the repetitive transients for the rotating machinery diagnosis. (C) 2016 Elsevier Ltd. All rights reserved