Grover V. Zurita

Grover V. Zurita
Universidad Privada Boliviana · Laboratory of Industrial Technology Innovation and robotics

Ph. D

About

45
Publications
20,467
Reads
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1,603
Citations
Citations since 2016
19 Research Items
1550 Citations
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2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
Introduction
Grover V. Zurita currently works at the Laboratory of Industrial Technology Innovation and robotics, Universidad Privada Boliviana. Grover does research in Automotive Systems Engineering, Mechanical Engineering and Automotive Engineering.
Additional affiliations
April 2017 - May 2017
Universidad Privada Boliviana
Position
  • Professor (Associate)
January 2017 - October 2017
Universidad Privada Boliviana
Position
  • Professor (Associate)
January 2003 - September 2014
Universidad Privada Boliviana
Position
  • Professor (Associate)

Publications

Publications (45)
Article
Full-text available
The induction motors (IMs) are undoubtedly the most used machines in industries because of the advantages they offer such as simplicity, service continuity and low cost. Due to wear and tear, the motor suffers different types of mechanical and electrical failures. Depending on the criticality of the plant motors, it could be necessary to implement...
Article
Full-text available
There is a steadily growing demand for reliable, versatile measurement rotor balancing system which can be used to determine the machine unbalances behavior. The effect of these causes are the increase of vibration amplitudes, causing damage to elements of the machines, mainly in the bearings, reduce useful life time, and increase fatigue failure i...
Article
Full-text available
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...
Article
Bearings are fundamental mechanical components in rotary machines (engines, gearboxes, generators, radars, tur- bines, etc) that have been identified as one of the primary causes of failure in these machines. This make bearing fault diagnosis (detection, classification and prognosis) an economic very relevant topic, as well as a technically challen...
Poster
Full-text available
Debido a sus capacidades de procesamiento, visualización, conectividad, y captura de imágenes, el uso de teléfonos inteligentes ha sido explorado recientemente como una opción factible para el desarrollo de instrumentos de medida en diferentes áreas (e.g. bio-sensores, espectrometría, sensores electro químicos). El presente trabajo presenta el desa...
Article
Full-text available
Resumen 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...
Article
Full-text available
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...
Article
Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches fo...
Article
Full-text available
Feature selection is an important aspect under study in machine learning based diagnosis, that aims to remove irrelevant features for reaching good performance in the diagnostic systems. The behaviour of diagnostic models could be sensitive with regard to the amount of features, and significant features can represent the problem better than the ent...
Article
Abstract 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...
Article
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 sensitiv...
Article
Gearboxes are crucial devices in rotating power transmission systems with applications in a variety of industries. Gearbox faults can cause catastrophic physical consequences, long equipment downtimes, and severe production costs. Several artificial neural networks, learning algorithms, and feature selection methods have been used in the diagnosis...
Article
Healthy rolling element bearings are vital guarantees for safe operation of the rotating machinery. Time-frequency (TF) signal analysis is an effective tool to detect bearing defects under time-varying shaft speed condition. However, it is a challenging work dealing with defective characteristic frequency and rotation frequency simultaneously witho...
Article
Full-text available
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 mai...
Article
Full-text available
This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal’s condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients’ energy content for terminal nodes is used as the input feature for...
Article
Full-text available
There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the...
Article
Gearboxes are crucial transmission components in mechanical systems. Fault diagnosis is an important tool to maintain gearboxes in healthy conditions. It is challenging to recognize fault existences and, if any, failure patterns in such transmission elements due to their complicated configurations. This paper addresses a multimodal deep support vec...
Conference Paper
Full-text available
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...
Article
Full-text available
Resumen 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...
Article
Full-text available
A new approach for reconstructing diesel engine cylinder pressure is presented. The technique is based on vibration measurements on the engine surface with subsequent reconstruction of the cylinder pressure by direct use of multivariate data analysis (MVDA). In order to investigate and evaluate the usefulness of the proposed technique, data from ea...
Article
Full-text available
Identification of the modal parameters is of major importance in characterizing the dynamic behaviour of structures and components, in particular to update finite element models, where parameters such as joint stiffness and damping cannot be estimated accurately. The main objective of this paper is to perform a comparison of several curve-fitting m...
Article
This paper focuses on the detection of knocking features in the combustion process based in time-frequency analysis using Choi-Williams distribution(CWD). The source data is the cylinder pressure from a Diesel engine. The resonances were carried out, the envelopes of each resonances were extracted and exponentially declining models were adapted. Wi...
Patent
The pressure in a cylinder of a combustion engine is determined on the basis of vibrations in the engine which are generated during movement of the piston. A vibration signal (S) from a vibration sensor situated on a cylinder is filtered in the region about the piston top dead center position, at the transition from compression to expansion, in a s...
Article
The general time frequency (TF)-adaptive window transform (GAWT) was used for the analysis and synthesis of time-varying signals of combustion noise. Noise emission from combustion engines was related to the vibrations in engine structure due to rapidly changing pressure in engine cylinders. The frequency of the resonances was found to depend on th...
Article
Full-text available
In this paper the study of non-stationary features due to resonances in the combustion process has been focused. The rapid rise in Diesel engines is recognised as an audible impulse noise, which is known as �knock�. The knock is caused by the spontaneous combustion of a significant volume of fuel/air mixture. The knock produces shock waves within t...
Article
For both engine designers and control purposes, a better understanding of the knocking that occurs during combustion in engines can lead to optimal decisions which will make engines operate more efficiently. The rapid rise in diesel engines is recognized as an audible impulse noise, which is known as ‘‘knock.’’ The knock is caused by the spontaneou...
Article
Road traffic noise is one of the most widespread and growing problems in urban areas. While it has long been known that hearing can be damaged by exposure to noise, it is also believed that continual noise, even at low or moderate intensity, can cause psychological discomfort and sleep disorders. Traffic noise levels in urban areas are increasing,...
Conference Paper
In diesel engines the combustion pressure waveform represents an ideal tool for detecting combustion anomalies. The cylinder pressure signal contains a lot of information about the condition of the engine and the combustion efficiency. It can also be used as identification of mechanical wears in the cylinders. There are growing demands for conditio...
Article
Includes bibliographical references. Thesis (doctoral)--Luleå University of Technology, 2001.

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