alaa jaber - Academia.edu (original) (raw)

Papers by alaa jaber

Engineering and Technology Journal

 Fabricate test rig to simulate the state and capture information  Extraction time domain signa... more  Fabricate test rig to simulate the state and capture information  Extraction time domain signal.  Transform time domain to frequency domain by FFT transform using sigview program.  Analysis result. Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor's current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.

International Journal of Renewable Energy Development

The fuel combustion in diesel engines can be improved by adding nanomaterials to the fuel which r... more The fuel combustion in diesel engines can be improved by adding nanomaterials to the fuel which result in an reduction in pollutant emissions and enhance the quality of fuel combustion. The engine performance and soot nanoparticles characteristics were evaluated in this study with adding nanoparticles of copper oxide (CuO2) to the rapeseed methyl ester (RME) and diesel under variable engine speeds. The addition of CuO2 to the RME significantly improve brake thermal efficiency (BTE) and decline the brake specific fuel consumption (BSFC) by 23.6% and 7.6%, respectively, compared to the neat RME and diesel fuel. The inclusion CuO2 nanoparticles into the RME and diesel led to decrease the concentration and number of particulate matter (PM)by 33% and 17% in comparison with neat RME and diesel without nano additives, respectively. Moreover, PM is significantly decreased by 31.5% during the RME combustion in comparison with neat RME and diesel under various engine speeds. It was also obtai...

Predictors of neck disability among undergraduate students: A cross-sectional study

Work

BACKGROUND: Many cross-sectional studies have examined the predictors of neck pain among adolesce... more BACKGROUND: Many cross-sectional studies have examined the predictors of neck pain among adolescents and working-age populations, but there are limited studies included undergraduate students. OBJECTIVE: To investigate the predictors of neck disability among undergraduate students. METHODS: A cross-sectional study using a self-administered online survey. Students completed the survey that included socio-demographic factors, academic-related factors, health and lifestyle factors, and standardized questionnaires including Neck Disability Index (NDI), 12-Item Short-Form Health Survey (SF-12), Depression Anxiety Stress Scales (DASS-21), and Pittsburgh Sleep Quality Index (PSQI). Students who reported an NDI score higher than 15 were considered as having a neck disability. A multivariable logistic regression model was used to identify the significant predictors of neck disability. RESULTS: Of all students (n = 1292), 20.8% reported neck disability. Among all possible predictors, students...

Industrial Robot Backlash Fault Diagnosis Based on Discrete Wavelet Transform and Artificial Neural Network

American journal of mechanical engineering, 2016

Industrial robots are commonplace in production systems and have long been used in order to impro... more Industrial robots are commonplace in production systems and have long been used in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The ability to continuously monitor the status and condition of robots has become a research issue in recent years and is now receiving considerable attention. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults (backlash) that could be progressed in the gearbox of industrial robot joints. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on Na...

Investigation of fluorescent lamp glass waste as a fluxing agent in porcelain bodies

Materials Today: Proceedings, 2021

Abstract In this research the effect of fluorescent lamp glass waste (FLGW) as a fluxing agent fo... more Abstract In this research the effect of fluorescent lamp glass waste (FLGW) as a fluxing agent for preparing porcelain bodies has been investigated. Three different standard mixtures of porcelain bodies were prepared which composed of (kaolin, flint and potash feldspar). The FLGW is added to the porcelain samples at different ratios of (0, 5 and 10) wt.% as a partial replacement of potash feldspar. The samples were prepared using semi-dry process and then sintered at different temperatures ranged of (925–1250)°C. Sintering behavior, physical and mechanical properties of the prepared porcelain bodies were measured. The results showed the possibility of using the FLGW as a fluxing agent for the production of porcelain bodies. Increasing of sintering temperature leads to enhance the physical and mechanical properties of porcelain bodies. Addition of FLGW as a partial replacement of potash feldspar in the porcelain composition batch has beneficial effect on lowering the sintering temperature of porcelain bodies, consequently of accelerating the densification rates. The physical and mechanical properties of porcelain bodies reduced when added of high amount of FLGW up to 10 wt%. This is related to the composition of FLGW that contents of different materials with the presence of impurities in considerable amounts.

A Data-Driven Approach Based Bearing Faults Detection and Diagnosis: A Review

IOP Conference Series: Materials Science and Engineering, 2021

Monitoring the condition of rotating machines is essential for system safety, reducing costs, and... more Monitoring the condition of rotating machines is essential for system safety, reducing costs, and increasing reliability. This paper tries to present a comprehensive review of the previously conducted research concerning bearing faults detection and diagnosis based on what is known as model-free or data-driven approaches. Mainly, two data-driven approaches are discussed, which are statistical-based approaches and artificial intelligence-based approaches. The employed condition monitoring techniques in diagnosing faults in different machinery are also deliberated. These include vibration, motor current signature, and acoustic emission signals analysis as they are widely utilized in condition monitoring based data-driven approaches. The advantages, limitations, and practical implications of each approach and technique are presented. However, it has been concluded that very few studies have adopted the statistical-based approach for bearings health monitoring. Thus, it is advised that ...

International Journal of Prognostics and Health Management, 2020

Maternal and Child Health Journal, 2020

Background One of the strictest quarantines worldwide to limit the spread of coronavirus was enfo... more Background One of the strictest quarantines worldwide to limit the spread of coronavirus was enforced in Jordan during the COVID-19 pandemic. Objectives This study investigated reported mental health and changes in lifestyle practices among Jordanian mothers during COVID-19 quarantine. The specific objectives included studying the level of depression, anxiety, and stress symptoms and their potential statistical associations with demographic and lifestyle variables. Furthermore, the study aimed to investigate differences in mental health between different demographic and socioeconomic groups and to examine the major lifestyle changes that occurred on mothers during the quarantine. Methods An online survey was developed and administered to 2103 mothers. Participants were asked to complete a sociodemographic data form, Depression, Anxiety, and Stress Scale (DASS-21), and a lifestyle section comparing the life of mothers before and during the quarantine. Reported scores of depression, anxiety, and stress were calculated and compared across different levels of demographics including income, education level, employment status, and city of residence. Results This study found that mothers with lower income, lower education, not employed, or living in cities outside the capital of Jordan reported having more depression, anxiety, and stress symptoms (p < .005). Changes in their lifestyle practices included weight gain, increased time allocated for teaching children at home, increased familial violence at home, and increased time allocated for caring for their family members (average increase of 5 hours daily). Conclusions for Practice The unprecedented times of quarantine have put mothers in unprecedented reported mental health problems. Providing psychological support to this group might be a priority.

Influence of fuel injection timing strategies on performance, combustion, emissions and particulate matter characteristics fueled with rapeseed methyl ester in modern diesel engine

Fuel, 2021

Abstract The combination between different conditions of fuel injection timings and biodiesel is ... more Abstract The combination between different conditions of fuel injection timings and biodiesel is a major challenge for the communities of vehicle research in terms of efficiency and emissions. The difficulties to achieving the emissions regulations in the recent years are linked to undesirable health effects and environmental impact. The effects of different engine conditions (injection timings and loads) on combustion, emissions, and particulate matter (PM) characteristics for diesel and biodiesel (B100) in diesel engine were experimentally investigated in this study. The combustion characteristics of cylinder pressure and rate of heat release (ROHR) were slightly higher during the combustion of B100 and advanced injection timing than during the combustion of diesel. Results have shown that THC and CO decreased from B100 combustion by 21% and 31% under 5 brake mean effective pressure (BMEP), respectively, and 32% and 46% under 2.5 BMEP of engine load compared to the diesel. Furthermore, advanced injection timing decreased CO and THC compared with retarded injection timing for B100 and diesel under both conditions of engine loads. However, nitrogen oxide (NOX) decreased (by 24%) with retarded injection timing and increased (by 7%) with advanced injection timing. Smoke number and particle number concentration decreased under conditions of advanced injection timing compared with retarded injection timings, especially during B100 combustion. In terms of particulate size, dpo decreased by 26.6 nm during B100 combustion and by 32.7 nm during diesel combustion under different conditions of injection timings and engine loads. The oxygen-bond in B100 contributed to important benefits in terms of NOX and PM without substantial influence on combustion characteristics and efficiencies.

Journal of Back and Musculoskeletal Rehabilitation, 2020

BACKGROUND: Work-related musculoskeletal disorders (WMSDs) represent a significant problem for nu... more BACKGROUND: Work-related musculoskeletal disorders (WMSDs) represent a significant problem for nurses. It is thus important to investigate nurses’ WMSDs prevalence and comprehensive predictors including motor, mental, and lifestyle factors. OBJECTIVES: To investigate the prevalence and predictors of lower quadrant WMSDs among Jordanian nurses. METHODS: A cross-sectional design, using self-administered questionnaires, was utilized. Outcome measures included Nordic Musculoskeletal Questionnaire (NMQ), Depression Anxiety Stress Scale (DASS21), Pittsburgh Sleep Quality Index (PSQI), sociodemographic data, and self-reported work ergonomics. Descriptive analyses were used to determine lower quadrant WMSDs prevalence and regression analyses were used to assess their predictors. RESULTS: A total of 597 nurses participated in the study. Twelve-month prevalence of lower quadrant WMSDs were 77.4% in lower back, 22.3% in hips, 37.5% in knees, and 28.5% in ankles and feet. Older age, longer year...

Time domain signal analysis to detect bearing faults using motor current signature analysis

2ND INTERNATIONAL CONFERENCE ON MATERIALS ENGINEERING & SCIENCE (IConMEAS 2019), 2020

International Journal on Advanced Science, Engineering and Information Technology, 2020

Bearings are critical components in rotating machinery. The need for easy and effective bearings ... more Bearings are critical components in rotating machinery. The need for easy and effective bearings fault diagnosis techniques has led to developing different monitoring approaches. In this research, however, a fault diagnosis system for bearings is developed based on the motor current signature analysis (MCSA) technique. Firstly, a test rig was built, and then different bearing faults were simulated and investigated in the test rig. Three current sensors, type SCT013, were interfaced to Arduino MEGA 2560 microcontroller and utilized together for data acquisition. The time-domain signals analysis technique was utilized to extract some characteristic features that are related to the simulated faults. It was noticed that the simulated bearing faults have led to generating vibrations in the induction motors, which in turn cause a change in its magnetic field. For classification (identification) of the extracted features, the artificial neural network (ANN) was employed. An ANN model was developed using the Matlab ANN toolbox to detect the simulated faults and give an indication about the machine health state. The obtained features from the captured motor current signals were utilized for training the ANN model. The results showed the effectiveness of using MCSA based on the timedomain signal analysis in combination with ANN in diagnosis different bearings faults.

International Journal of Electrical and Computer Engineering (IJECE), 2017

Industrial robots are now commonly used in production systems to improve productivity, quality an... more Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring...

International Journal on Advanced Science, Engineering and Information Technology, 2019

Predicating the required building energy when it is in the design stage and before being construc... more Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system. Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.

International Journal of Electrical and Computer Engineering (IJECE), 2018

Signal processing plays a significant role in building any condition monitoring system. Many type... more Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.

Insight - Non-Destructive Testing and Condition Monitoring, 2016

Industrial robots have long been used in production systems in order to improve productivity, qua... more Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot. _____________________ Alaa Abdulhady Jaber et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

International Journal of Electrical and Computer Engineering (IJECE), 2016

Machine healthy monitoring is a type of maintenance inspection technique by which an operational ... more Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines....

The optimum selection of wavelet transform parameters for the purpose of fault detection in an industrial robot

2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), 2014

Industrial robots are commonly used in production systems in order to improve productivity, quali... more Industrial robots are commonly used in production systems in order to improve productivity, quality and safety in manufacturing. There are many functions that can be carried out by industrial robots, and they represent the basic building blocks of the production sector. The ability to continuously monitor the status and condition of robots has become an important research issue in recent years and is now receiving considerable attention. Many types of signals can be used for the detection of faults in industrial robots, such as vibrations and acoustic emissions. However, the most important thing is how these signals are processed in appropriate ways in order to extract the most salient features related to specific robot faults. Thus, signal processing step plays a significant role in the fault detection process for any machine and especially for industrial robots. Therefore, the wavelet transform has been utilized in this research for the detection of faults in an industrial robot. In order to build an accurate fault detection system a number of parameters in the wavelet analysis need to be adjusted carefully. The main focus of this research is to discuss the appropriate selection of these parameters, and then to build a fault detection system for the robot based on LabView programming.

2014 European Modelling Symposium, 2014

The condition monitoring of machines has long been accepted as a most effective solution in avoid... more The condition monitoring of machines has long been accepted as a most effective solution in avoiding sudden shutdown and to detect and prevent failures in complex systems. Signal capture and analysis, and feature extraction and classification represent the main tasks in building any monitoring system. Signal processing plays a significant role in condition monitoring and the fault diagnosis process. Many types of signals can be used in the condition monitoring of machines, such as vibration, electrical and sound signals. Processing these signals in an appropriate way is crucial in extracting the most salient features related to specific types of faults. A variety of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. The main focus of this research is a consideration of signal processing techniques which can be applied in condition monitoring, and to identify their advantages and disadvantages. Then, the wavelet transform is discussed in detail. After that, a monitoring system based on multi-resolution analysis using the wavelet transform is successfully simulated using LabVIEW and Matlab capabilities. The results show that the differences between healthy and faulty signals can be effectively detected using the wavelet transform.

International Journal of Materials Science and Engineering, 2015

This paper deals with on-line (Real-time) multiresolution signal analysis using wavelet transform... more This paper deals with on-line (Real-time) multiresolution signal analysis using wavelet transform by means of graphical programming using LabVIEW. A system for wavelet analysis has been designed based on Arduino-Uno board interfaced to LabVIEW. To achieve the interfacing LIFA has been used. Firstly, the noise from the signal was remove using wavelet transform, and then the de-noised signal analyzed to multi-level frequency bands. A Matlab code to do wavelet analysis was written in a Matlab script node in LabVIEW. To test this system, a vibration signal form robotic arm was captured and analyzed using this system, and the result utilized to establish if there is fault in the robot. The result showed that multi-resolution analysis can be achieved efficiently using this system and can be applied in many applications. 