Nazzal Salem
@zu.edu.jo
Mechanical engineering department / Faculty of engineering
Zarqa University
Scopus Publications
Scopus Publications
G Grebenişan, N Salem, S Bogdan, and D C Negrău
IOP Publishing
Abstract This paper aimed to validate a working tool, component of the Predictive Maintenance Toolbox ™, produced by Matlab (MathWorks), in the case of a procedure for monitoring the operation of mechanical systems, in order to diagnose a failure of the process and to estimate the remaining useful life (RUL). This toolbox provides toolsets, materialized in function files, for labeling data, designing condition indicators, and estimating a parameter named the remaining useful life of a machine. You can analyze and label machine data imported from local files, cloud storage, and distributed file systems. The algorithm suggested by Matlab (software owned by MathWorks) was used in detail to process part of the data set provided freely by NASA through The Prognostics Data Repository, The Prognostics Center of Excellence (PCoE) at Ames Research Center. Of the 4 data sets, only one was used for this paper. Each data set is composed of 3 working files, in text format, for training, test and algorithm validation, and solution statement, respectively. The results obtained confirm the validity of the computer-assisted training system, diagnostics, prognosis, and validation tools, on a statistical basis, in the case of consistent databases.
G Grebenişan, N Salem, S Bogdan, and D C Negrău
IOP Publishing
Abstract In this paper was used the data set collected in a research project between private companies from Romania and Italy, for the development of a basic approach of artificial neural network techniques, as an application in Matlab, aiming to detect the degree of degradation of oil, an automated installation, measuring online the physicochemical properties of the oil. Physical-chemical parameters measured lead to the creation of generous time series, but accessible by numerical and statistical calculation, for the application of artificial intelligence techniques. Applying neural network techniques to parameters that measure oil degradation, oxidation and humidity have generated the results of this work. The main function of monitoring the state of operation of a mechanical system, machine, or plant is to provide the almost correct diagnosis of the machine’s state and rate of change so that preventive measures can be taken at a given time.
G Grebenişan, S Bogdan, N Salem, and D C Negrău
IOP Publishing
Abstract Condition monitoring and machine status classification are of great practical importance in the manufacturing industry as it provides online updates on the state of the machine, avoiding loss of production and minimizing the probability of generating catastrophic damage to the machine. In this paper, the classification of conditions is based on the processing of information using wavelets based on the results of the monitoring and the data collected during such an action, measuring the characteristics of the lubricating oil over some time sufficient to produce a time series of results. In this paper, the classification system is tested and validated using observation sequences based on the maximum wavelet distribution obtained from the collected signals, monitoring the state of the lubricating oil, to define and diagnose singularities in time series.
Nazzal Salem
IOP Publishing
Experimental studies of improvement of the quality of gravity die casting by a low-frequency vibration treatment were carried out. The objective of the study was to establish in principle the possibilities of applying the method of vibration effect in the gravity die casting process for improving the production efficiency. Different testing methods established the advantage of the proposed method comparing to the basic one. The use of the technology of interest reduces a number of casting defects and can be recommended for use in conditions of large production capacities.
Gavril Grebenişan, Nazzal Salem, and Sanda Bogdan
EDP Sciences
This paper addresses a delicate problem, namely the diagnosis of the state of the oils in the industrial systems, namely the machine tools. Based on measurements (the data set contains over five million records), within a Machine Intelligence for Diagnosis Automation (MIDA) project funded by the National Program PN II, ERA MANUNET: NR 13081221 / 13.08.2013, several applications of MATLAB toolbars are being developed in the field of artificial intelligence, specifically using the Support Vector Machine algorithms and neural networks. The tests were carried out on several distinct situations, followed by validation and verification tests on the devices designed and developed within the project (MIDA, Monitoil).
Gavril Grebenişan, Nazzal Salem, and Sanda Bogdan
EDP Sciences
This approach is focused on Machine Intelligence for Diagnosis Automation, a research program, which promotes « preventative maintenance in manufacturing plants through the development of a fully automated prototype for oil analysis and fault prediction. The prototype is based on Artificial Intelligence (A.I.) software and online hardware ». Monitoring the condition of lubricants requires the examination of the physical, chemical and additive states, which maintain the quality of the lubricants, which is necessary for the proper functioning of the equipment. A lubricant monitoring program, especially from a qualitative point of view, will need to focus on both machine tool wear and degradation of lubricants, as well as on detecting and describing abnormal working conditions for both lubricants and machine parts. This goal can be satisfied by examining all the oils used in a company by completing laboratory tests to generate steps and acceptance classes, as well as unplanned contingency analyzes. These laboratory tests can be concentrated and classified into technology-based data sheets based on test-based information and test results, ultimately constituting consistent databases needed to generate monitoring and prevention reports. Data on the condition of the oil parameters used in the hydraulic system for lubricating machine tools have been collected during six months. The data as matrix organized, with 258648 rows (observations) and 21 columns (parameters).
Gavril Grebenişan and Nazzal Salem
EDP Sciences
In the industrial practice, the product is intended to be flawless, with no technological difficulty in making the profile shapes. If this product results without defects, then any Finite Elements Method (FEM) based simulation can support that technology. A technology engineer does not propose, very often to analyze the simulation of the design technology, but rather to try to optimize a solution that he feels feasible. Experiments used as the basis for numerical optimization analysis support their research in the field of superplastic forming. Determining the influence of input parameters on the output parameters, Determining the optimal shape of the product and the optimal initial geometry, the prediction of the cracks and possibly the fractures, the prediction of the final thickness of the sheet, these are the objectives of the research and optimization for this project. The results of the numerical simulations have been compared with the measurements made on parts and sections of the parts obtained by superplastic forming. Of course, the consistency of the results, costs, benefits, and times required to perform numerical simulations are evaluated, but they are not objectives for optimizing the superplastic forming process.