Papers by Yasir Hassan Ali
Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to commun... more Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte's algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets.
Reciprocating compressor is one of the most popular classes of machines use with wide application... more Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future. , 0 (2019) MATEC Web of Conferences
Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in m... more Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.
achieving the highest performance and efficiency of elevators have become an important area of re... more achieving the highest performance and efficiency of elevators have become an important area of research in the world of elevators. Because of the accelerating nature of the world, the interest in time and the rush to accomplish business became a major need for mankind. And that high buildings rely mainly on elevators, reducing the response time of the elevator is the most important research areas, so in this research, we propose an idea to provide the elevator to the person and not to false demand and this requires detection the presence of someone waiting for the elevator to the proposed system provides an algorithm based on the sensor PIR (passive infrared sensor). The results showed the size of profit achieved by the system and the profit is divided into profit times (reduce response time which leads to increase comfort for the rest of the people) and profit in reducing the energy consumed by the elevator.
As an important part of rotating machinery, bearing state affects the whole effectiveness and sta... more As an important part of rotating machinery, bearing state affects the whole effectiveness and stability of machine components. Recently, many condition monitoring techniques have been developed for bearing fault detection and diagnosis to avoid malfunctioning during operation that might lead to catastrophic failures or even deaths. Vibration monitoring technique is the mostly used as it is cost-effective to detect, locate and estimate bearing faults. Within the technique, the time domain features are favourable to be used for fault machinery faults detection and diagnosis. This is due to its advantages, including it contains all the machine faults information and possibility of using much data for easy and clear fault diagnosis. This study proposes a diagnosis model for bearing faults in rotating machinery based on time domain features and binary logistic regression (BLR) modelling technique of a vibration signals. The steps of the new fault prediction method for bearings are as follows. First, vibration data were collected. Second, the effective time domain parameters extraction from the acquired vibration data sets using multivariate analysis of variance (MANOVA). Third, the data-splitting technique was employed. Here the predictive modelling was performed based on the BLR modelling technique by using the most salient time domain parameters of bearing fault state on the training data set and the selected BLR model was internally validated by using the testing data set. Finally, a comparison was made between the selected BLR model and an artificial neural network model with regards to their accuracy, computational efforts, and effectiveness. The results show the effectiveness and plausibility of the proposed method, which can support timely maintenance decisions in order to facilitate machine performance and fault prediction and to prevent catastrophic failures.
This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wa... more This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wavelet transform and casing vibration. The common detection method (FFT) is effective only if sever blade faults occurred while the minor faults usually remain undetected. Wavelet analysis as alternative technique is still unable to fulfill the fault detection and diagnosis accurately due to its inadequate time-frequency resolution. In this paper, wavelet is adapted and its time-frequency is improved. Experimental study was undertaken to simulate multi stages rotor system. Results showed that the adapted wavelet analysis is effective in twisted blade diagnosis compared to the conventional one.
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Papers by Yasir Hassan Ali