Browsing by Autor "Grover Zurita"

Now showing 1 - 13 of 13

A COMPARATIVE OF CURVE-FITTING ALGORITHMS FOR THE EXTRACTION OF MODAL PARAMETERS FROM RESPONSE MEASUREMENTS
(2004) Robert B. Randall; Grover Zurita; T. Wardrop
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.
A COMPARATIVE OF CURVE-FITTING ALGORITHMS FOR THE EXTRACTION OF MODAL PARAMETERS FROM RESPONSE MEASUREMENTS
(2005) Robert B. Randall; Grover Zurita; T. Wardrop
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.
A REVIEW OF VIBRATION MACHINE DIAGNOSTICS BY USING ARTIFICIAL INTELLIGENCE METHODS
(2016) Grover Zurita; René–Vinicio Sánchez; Diego Cabrera
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.
A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis
(Institute of Electrical and Electronics Engineers, 2018) Chuan Li; José Valente de Oliveira; Mariela Cerrada; Diego Cabrera; René–Vinicio Sánchez; Grover Zurita
Bearings are fundamental mechanical components in rotary machines (engines, gearboxes, generators, radars, turbines, etc.) that have been identified as one of the primary causes of failure in these machines. This makes bearing fault diagnosis (detection, classification, and prognosis) an economic very relevant topic, as well as a technically challenging one as evaluated by the extensive research literature on the subject. This paper employs a systematic methodology to identify, summarize, analyze, and interpret the primary literature on fuzzy formalisms for bearing fault diagnosis from 2000 to 2017 (March). The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis (summary, classification, and critique) of the available approaches resorting to fuzzy formalisms in this trendy topic. A discussion on a new promising future research direction is provided. A comprehensive list of references is also included.
Applications of Operational Modal Analysis in Gearbox and Induction Motor, Based on Random Decrement Technique and Enhanced Ibrahim Time Method
(Multidisciplinary Digital Publishing Institute, 2022) Gabriel Castro; Grover Zurita
There have been steadily growing requirements from the academia and industry, demanding non-invasive methods and reliable measurement systems of research devoted to operational mode analysis (OMA). Due to the simplicity of performing only structures surface vibration measurements, OMA is frequently applied in machine fault diagnosis (MFD) and structure health monitoring (SHM). OMA can handle big structures, such as bridges, buildings, machines, etc. However, there is still an open question: how to properly handle the harmonic effects of rotating components and the difficulty of closely estimating space modes are still a nightmare to deal with. Therefore, the main objective of this paper is to identify the structure of natural frequencies by the regeneration of frequency response functions (FRFs) for complex structures based on OMA. The novelty of our approach is to use the random decrement technique (RDT), correlation function estimation (CFE), and enhanced Ibrahim time method (EITM) to overcome OMA’s difficulties and limitations. To reduce further rotational harmonics effects, gear mesh and side band frequencies, digital signal processing techniques based on notching filters, and liftering analysis techniques were also used. All the experiments were performed at the laboratory test rig and conducted by using three accelerometers, one impedance hammer, one force sensor, and one data acquisition board. To reduce data’s variabilities, each test was measured three times for 5 min. The data sampling frequency for all the experiments was 25.6 kHz. To validate the proposed methodology, extensive OMA tests were performed for the generation of FRFs. The measured objects were a steel bar, induction motor, and gearbox. Five structural natural frequencies for the induction motor and eight structural natural frequencies for the gearbox were generated, respectively.
Development of a Low-Cost Vibration Measurement System for Industrial Applications
(Multidisciplinary Digital Publishing Institute, 2019) Adrian Villarroel; Grover Zurita; R. C. Velarde-Montecinos
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.
Digital FIR filter design for diagnosing problems in gears and bearings using Xilinx's system generator
(2014) Mijail Vidal; Rodrigo Cruces; Grover Zurita
This paper discusses a common problem in industry to detect machine failures, especially in gears and bearings through vibrational analysis. Vibrational analysis is a powerful method for analyzing any machine in the industry, but this method uses expensive signal conditioning/processing devices and significant expertise for the analysis of signals in the frequency domain. In this context, this study proposes a new system of failure detection in machine can be done with digital Finite Impulse Response (FIR) filters. The systems of data Adquisition are compose with the test machine, accelerometer (model-353B17) and PCI-4474 Data Acquisition Card. The data from test machine are evaluated in both optimal and failure conditions. To evaluate the FIR filters system, a system was designed with the Xilinx System Generator. The structure design had four filters for evaluating gears and bearings separately. In failure condition the filters 1, 2, 3 detected failures in their amplitudes, but for bearings just the filter 4 detected a failure. To validate the system it was implemented in a Field Programmable Gate Array (FPGA) Spartan 3E where the design optimizes hardware resources and has the same responses than Xilinx System Generator. The response of the filters was evaluated with the ISO 10816-3 standard.
Fault diagnosis in spur gears based on genetic algorithm and random forest
(Elsevier BV, 2015) Mariela Cerrada; Grover Zurita; Diego Cabrera; René–Vinicio Sánchez; Mariano Artés; Chuan Li
Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition
(Higher Education Press, 2015) Diego Cabrera; Fernando Sancho; René–Vinicio Sánchez; Grover Zurita; Mariela Cerrada; Chuan Li; Rafael E. Vásquez
Observer-biased bearing condition monitoring: From fault detection to multi-fault classification
(Elsevier BV, 2016) Chuan Li; José Valente de Oliveira; Mariela Cerrada; Fannia Pacheco; Diego Cabrera; René–Vinicio Sánchez; Grover Zurita
SHAFT ALIGNMENT MEASUREMENT SYSTEM DEVELOPED FOR INDUSTRIAL APPLICATIONS
(2018) Ivan D Mendoza; Grover Zurita
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.
UNA REVISIÓN BIBLIOGRÁFICA DEL ANÁLISIS VIBRACIONAL PARA EL DIAGNÓSTICO DE MÁQUINAS MEDIANTE EL USO DE MÉTODOS DE INTELIGENCIA ARTIFICIAL
(2016) Grover Zurita; Vinicio Mora Sánchez; Diego Cabrera
VIBRATION BASED RECONSTRUCTION OF THE CYLINDER PRESSURE IN DIESEL ENGINES BY USING NEURAL NETWORK
(2005) Juan Carlos Peña; Grover Zurita
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.