Farzin Piltan

• PhD
• Professor (Research) at University of Ulsan

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and moto...

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain...

In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination...

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective...

Dear Colleagues, Machinery and mechanical structures in the industry suffer from inevitable degradation and performance degradation during operation. By collecting and processing data using a variety of sensors, timely diagnosis of symptoms of deterioration and reliable estimation of future health conditions are essential for industrial productivi...

Bearing faults can lead to costly downtime, equipment damage, and compromised safety in various industries. To address these challenges, "Bearing Fault Diagnosis: A Comprehensive Guide" offers a practical and insightful exploration of fault diagnosis methods specifically tailored to bearings. This comprehensive book serves as an invaluable resource...

Bearings are critical components of motors. However, they can cause several issues. Proper and timely detection of faults in the bearings can play a decisive role in reducing damage to the entire system, thereby reducing economic losses. In this study, a hybrid fuzzy V-structure fuzzy fault estimator was used for fault diagnosis and crack size iden...

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout l...

Rolling bearings are treated as important machinery power components, faults of rolling bearings affect machinery operation, so an intelligent fault diagnosis method is very useful of safety operation in rolling bearings. This paper proposes a novel fault diagnosis method based on improved Adaptive Deep Convolution Neural Networks algorithm to real...

In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component...

SI: The Role of Data Science, and Computer Vision in Public Health The use of massive amounts of data has become critical in the public health sector. We have seen several cases throughout this COVID-19 outbreak. We have witnessed significant advancements in cancer detection, COVID x-ray processing, brain signal analysis, and stress analysis using...

Rotating machinery plays an important role in industrial systems, and faults in the machinery may damage the system health. A novel image-based diagnosis method using improved deep convolutional generative adversarial networks (DCGAN) is proposed for the feature recognition and fault classification of rotating machinery. First, vibration signal dat...

Active acoustic emission (AE) signal estimation is crucial for realizing high-precision bearing fault diagnosis. However, the identification of the bearing fault in the low-speed motor is still a challenging issue. In this article, observer-based low-speed bearing fault identification is investigated, and an observer with adaptive fuzzy switching g...

Bearings cause the most breakdowns in induction motors, which can result in significant economic losses. If faults in the bearings are not detected in time, they can cause the whole system to fail. System failures can lead to unexpected breakdowns, threats to worker safety, and huge economic losses. In this investigation, a new approach is proposed...

Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the b...

Acoustic emission techniques are widely used to monitor industrial pipelines. Intelligent methods using acoustic emission signals can analyze acoustic waves and provide important information for leak detection and localization. To address safety and protect the operation of industrial pipelines, a novel hybrid approach based on acoustic emission si...

Bearings are used to reduce inertia in numerous utilizations. Lately, anomaly detection and identification in the bearing using acoustic emission signals has received attention. In this work, the combination of the machine learning and adaptive-backstepping digital twin approach is recommended for bearing anomaly size identification. The proposed a...

Pipelines are used to transport liquids and gases between different places. In recent years, identifying the location and size of cracks in transmission pipes has received much attention. Thus, in this research, the combination of fuzzy digital twin (FDT), support vector machine (SVM), and backstepping (BS) observer is suggested for leak detec...

A rub-impact fault is a complex, nonstationary, and nonlinear fault that occurs in turbines. Extracting features for diagnosing rubbing faults at their early stages requires complex and computationally expensive signal processing approaches that are not always suitable for industrial applications. In this article, a hybrid approach that uses a comb...

This Special Issue aims to present original research papers with high quality and novelty and also review papers on “Data Analytics in Energy Systems”. Topics of interest include but are not limited to: Data analytics for energy system operation and control; Multimodal data analytics and fusion; Distributed data mining; Artificial intelligence, m...

Induction motors are consumed around 80% of energy in heavy industries, that approximately 20% of this energy consumption is because of mechanical failures. Moreover, the bearing failure with about 69% is the principal constituent of mechanical defects. In this study, the self-tuning intelligence digital twin is presented for bearing pattern recogn...

In this research, advanced technology is used to monitoring chaotic time-series signals. The combination of autoregressive with adaptive network-fuzzy algorithms is suggested for chaotic signal prediction. The autoregressive prediction algorithm is recommended for chaotic time-series prediction. This technique is linear, and the modeling prediction...

In this research, the combination of the smart digital twin (SDT) and the machine learning technique is prescribed to have a reliable fault pattern recognition in this effort. In the first stage, the SDT for the bearing is designed by the dynamical system modeling, updated using the data-driven autoregression approach, and estimate the performance...

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective...

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated...

Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore , a deep learning-based adaptive neural-fuzzy structure technique...

. Rolling element bearing (REB) represent a class of nonlinear and multiple-degrees-of-freedom rotating machines that have pronounced coupling effects and can be used in various industries. The challenge of understanding complexity in a bearing’s dynamic behavior, coupling effects, and sources of uncertainty presents substantial challenges regardin...

Inner, outer, and ball faults are complex non-stationary and non-linear faults that occurs in rotating machinery such as bearings. Designing an effective procedure for fault diagnosis (FD) is essential to safe operation of bearings. To address fault diagnosis issue, a robust, hybrid technique based on the ARX-Laguerre fuzzy-sliding proportional int...

Bearings are complex components with nonlinear behavior that are used to reduce the effect of inertia. They are used in applications such as induction motors and rotating components. Condition monitoring and effective data analysis are important aspects of fault detection and classification in bearings. Thus, an effective and robust hybrid techniqu...

Inner, outer, and ball faults are complex non-stationary and non-linear faults that occurs in rotating machinery such as bearings. Designing an effective procedure for fault diagnosis (FD) is essential to safe operation of bearings. To address fault diagnosis issue, a robust, hybrid technique based on the ARX-Laguerre fuzzy-sliding proportional int...

Bearings are complex components with nonlinear behavior that are used to reduce the effect of inertia. They are used in applications such as induction motors and rotating components. Condition monitoring and effective data analysis are important aspects of fault detection and classification in bearings. Thus, an effective and robust hybrid techniqu...

Robotic manipulators represent a class of nonlinear and multiple-degrees-of-freedom robots that have pronounced coupling effects and can be used in various applications. The challenge of understanding complexity in a system’s dynamic behavior, coupling effects, and sources of uncertainty presents substantial challenges regarding fault estimation, d...

A blade rub-impact fault is one of the complex and frequently appearing faults in turbines. Due to their nonlinear and nonstationary nature, complex signal analysis techniques, which are expensive in terms of computation time, are required to extract valuable fault information from the vibration signals collected from rotor systems. In this work, a...

In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibr...

Rolling-element bearings (REBs) make up a class of non-linear rotating machines that can be applied in several activities. Conceding a bearing has complicated and uncertain behavior that exhibits substantial challenges to fault diagnosis. Thus, the offered anomaly-diagnosis algorithm, based on a fuzzy orthonormal-ARX adaptive fuzzy logic-structure...

Featured Application: Fault diagnosis and fault-tolerant control. Abstract: A robot manipulator is a multi-degree-of-freedom and nonlinear system that is used in various applications, including the medical area and automotive industries. Uncertain conditions in which a robot manipulator operates, as well as its nonlinearities, represent challenges...

The design of an effective procedure for leak detection, estimation, and leak size classification is necessary to maintain the healthy and safe operations of pipelines for conveying fluids and gas from one place to another. The complexities of nonlinear and uncertain behavior inherent in a pipeline lead to difficulty of detection, estimation, and l...

Designing an effective procedure for fault detection and identification (FDI) is necessary to maintain the healthy and safe operation of robot manipulators. The complexities of nonlinear parameters inherent in a robot manipulator make it challenging to detect and identify faults. To address this issue, a powerful, robust, hybrid fault identificatio...

Pipes are widely used in industries for conveying fluids and gas from one place to another. The pipeline network is subjected to several problems, such as surface load, lousy quality, pitting corrosion, and water hammer, which cause cracks in a pipeline or joint. Complexities of nonlinear parameters inherent in a pipeline prohibit the detection and...

Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system's dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenge...

Air Quality Indexes (AQI) play more important roles in various types of cancers. Control and reduce the rate of various types of pollutions such as sulfur dioxide are the basic requirements to reduce the rate of cancers. Besides, to improve the science of cancer detection, increase the rate of AQI with an effective assessment of the healthy conditi...

Pipes are widely used in industries for conveying fluids and gas from one place to another. The pipeline network is subjected to several problems, such as surface load, lousy quality, pitting corrosion, and water hammer, which cause cracks in a pipeline or joint. Complexities of nonlinear parameters inherent in a pipeline prohibit the detection and...

Designing an effective procedure for fault detection and identification (FDI) is necessary to maintain the healthy and safe operation of robot manipulators. The complexities of nonlinear parameters inherent in a robot manipulator make it challenging to detect and identify faults. To address this issue, a powerful, robust, hybrid fault identificatio...

Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surg...

Convergence speed for system identification and estimation is a popular topic for determining the kinematics and dynamic identification/estimation of the parameters of robot manipulators. In this paper, adaptive fuzzy inverse dynamic system estimation is used to improve robust modeling, especially for a serial links robot manipulator. The Lyapunov...

In practical applications, modeling of real systems with unknown parameters such as distillation columns are typically complex. To address issues with distillation column estimation, the system is identified by a proposed intelligent, auto-regressive, exogenous-Laguerre (AI-ARX-Laguerre) technique. In this method, an intelligent technique is introd...

In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy sliding mode extended autoregressive exogenous input (ARX)–Laguerre proportional integral (PI) observer is proposed. The proposed T–S fuzzy sliding mode extended-state ARX–Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection...

The main reasons to use fuzzy logic technology are ability to give approximate recommended solving unclear and complex problem, easy to understand, and flexible then a designer is able to model controller for any nonlinear plant with a set of IF-THEN rules, or it can identify the control actions and describe them by using fuzzy rules. It must be no...

This paper proposes an extended-state ARX-Laguerre proportional integral observer (PIO) for fault detection and diagnosis (FDD) in bearings. The proposed FDD technique improves fault estimation using a nonlinear function while generating a robust residual signal using the sliding mode technique, which can indirectly improve the performance of FDD....

This paper proposes a reliable, intelligent, model-based (hybrid) fault de-tection and diagnosis (FDD) technique for wireless sensor networks (WSNs) in the presence of noise and uncertainties. A wireless sensor network is a network formed by a large number of sensor nodes in which each node is equipped with a sensor to detect physical phenome-na su...

The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer (FLO). Th...

The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer (FLO). Th...

Dynamic of Robot Manipulator and implement it using MATLAB

This lecture describes forwarding kinematics in the robot manipulator.

This ppt is about the introduction of the robot manipulator.

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mod...

The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARXLaguerre method was used in this study to dynamic mo...

This paper proposes a model-based fault detection and diagnosis (FDD) technique for six degrees of freedom PUMA robot manipulator in presence of noise in actuator and sensor faults. The inverse modeling based on an adaptive method, which combines the fuzzy C-means clustering with the modified autoregressive eXternal (ARX) model, is presented for th...

The main contribution of this paper is the design of a robust model reference fuzzy sliding mode observation technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. A fuzzy sliding mode controller was used in this study to control the robot manipulator in the presence of uncertainty and disturbance. T...

This paper describes the design of a robust composite high-order super-twisting sliding mode controller (HOSTSMC) for robot manipulators. Robot manipulators are extensively used in industrial manufacturing for many complex and specialized applications. These applications require robots with nonlinear mechanical architectures, resulting in multiple...

This paper presents a stable ARX-Laguerre fuzzy proportional-integral-derivative observation (FPIDO) system for fault detection and identification (FDI) of actuator and sensor faults in a multi-degrees of freedom robot manipulator. An ARX-Laguerre technique is used in this paper to improve the system modeling in the presence of uncertainty and dist...

The main objective of this paper is the design of a robust and stable multivariable decoupling based Proportional-Integral-Derivative (PID) like fuzzy scheduling technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical distillation column. A PID like fuzzy scheduling controller was used in this paper to control the distil...

The main objective of this paper is the design of a robust and stable multivariable decoupling based Proportional-Integral-Derivative (PID) like fuzzy scheduling technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical distillation column. A PID like fuzzy scheduling controller was used in this paper to control the distil...

This paper presents a stable neuro-fuzzy based Auto-Regressive with eXternal model input (ARX) sliding mode methodology for fault detection and tolerant (FDT) in a DC motor. A neuro fuzzy ARX technique is used to improve the system modeling and identification in the presence of uncertainty and disturbance in a DC motor. The proposed model reference...

In this research paper, nonlinear FPGA-based feedback linearization position controller is recommended for serial links robot manipulator. Robot manipulators are multi-input multi-output (MIMO), nonlinear, time variant, uncertain dynamic systems and are developed either to replace human work in many fields such as in industrial or in the manufactur...

The Proportional-Integral-Derivative (PID) controller to control of first order delay system has fluctuations in presence of uncertainty. To reduce the rate of fluctuations as well as improve the first order delay rise time, the first objective is design a Proportional- Integral-Integral-Derivative (PI2D) controller. This algorithm is complex contr...

Control of system’s temperature is one of the active research areas in field of energy consumption. In this research we have following objectives: temperature data collection from system, intelligent system identification based on neuro-fuzzy Auto Regressive eXternal model input (ARX) methodology and design a nonlinear controller to fixed a tempera...

Robust controller design for nonlinear systems with unknown dynamics (delay system) the impact of intense activity between connections can be considered as a challenge in this research. In order to reduce delays in the system resistant and the non-linear technique called variable structure control method is used. Variable structure control method i...

System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algor...

Medical robots are sensitive tools to improve the surgery's performance. One of the most active research area in this field is control of medical robot. In this research, nonlinear, stable and robust Sliding Mode controller (SMC) is used as a based controller. This algorithm works based on the functional operation. The main traditional functions fo...

In this research, Neuro-fuzzy fuzzy feedback linearization controller is recommended for sensitive three degrees of freedom dental actuator. To design stable high quality controller conventional feedback linearization controller is recommended. Conventional feedback linearization (FL) controller is a nonlinear, stable, and reliable controller. This...

One of the most familiar challenge of air pollution over the cities is smog hanging. The effects of inhaling particulate matter have been studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. There are, however, some additional products of the combustion process that include nitrogen oxides and s...

System identification is one of the main challenges in real time control. To design the best controller for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. The second important challenge in the field of control theory is, design high-perfo...

Design a robust oscillation-free controller for multi input-multi output (MIMO) nonlinear uncertain dynamical system (sensitive dental joint) is the main objective in this research. In this paper, robust sliding mode controller will be selected as a main control technique and linear controller will be design to improve the stability and robustness...

It goes without saying that, there is quite a diverse mixture of the linear controller. Nevertheless, I assume the most famous would probably be Proportional Integral (PI) controller. The things with PI controller are that most of PI controllers are reduction the error. As well as PI controllers, another kind of linear controller worth mentioning c...

This research paper focuses on the design and analysis of a high performance adaptive PD like fuzzy computed Torque controller for second order nonlinear uncertain (medical robot manipulator) system, in presence of uncertainties. The proposed approach effectively combines of design methods from Computed Torque controller (CTC), adaptive controller...

Many of fuzzy control applications require real-time operation; higher density programmable logic devices such as field programmable gate array (FPGA) can be used to integrate large amounts of logic in a single IC. This work, proposes a developed method to fuzzifier algorithm with optimal-tunable gains method-using FPGA. The maximum frequency in FP...

This paper describes the design intelligent based nonlinear controller for three dimensions joint dental robot manipulators in presence of uncertainty. Three-dimension joint dental robot manipulator has some important challenges such as nonlinearity, multi-input multi-output (MIMO), uncertainty in dynamic formulation, and coupling effect. To elimin...

IN THIS BOOK THE BASIC INFORMATION IN FIELD OF ELECTRIC CIRCUIT IS INTRODUCED. THIS BOOK HAS MANY EXAMPLES.

Recent developments of robotics allocated many of industrial and medical activities. So that most of industries turned to use surgical robots in their production line or in their surgery. Being precise, spent less time-consuming, present uniform quality with less cost and reducing waste and energy are some advantages of using robots in industry. Th...

This book is an undergraduate level textbook presenting a thorough discussion of state-of-the-art digital devices and circuits. It supplements our Research and Development Company, SSP.Co. It is selfcontained; begins with the basics and ends with the latest developments of the digital technology. The intent is to prepare the reader for advanced di...

The dynamics of a first order delay system is highly nonlinear, time variant, uncertain and coupling effects. The main objectives to control of first order delay system are time response and acceleration measurements. The problem of acceleration measurements can be reduced, based on design sensor-less Proportional-Integral-Derivative (PID) filter c...

Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This research focuses on the design, and analysis of a model-reference sliding mode controller for first order delay system, in presence of uncertainties. In order to provide high performance nonlinear methodology, mo...

The objective of this paper is to design and coordinate controllers that will enhance transient stability of three dimensions motor subject to large disturbances. Two specific classes of controllers have been investigated, the first one is a type of disturbance signals added to the excitation systems of the generating units. To address a wide range...

Uncertain or complicated systems are difficult to control. Modeling the system uncertainties is an especial topics in most of engineering field. On the other hand, since system has uncertainty, design stable and robust controller is crucial importance in control engineering. To solve this challenge nonlinear control technique is the best choice. Sl...

Most of controllers need real time mobility operation so one of the most important devices which can be used to solve this challenge is Field Programmable Gate Array (FPGA). FPGA can be used to design a controller in a single chip Integrated Circuit (IC). To have higher implementation speed with good performance cMinimum Control Unit (MCU) is imple...

In this paper, a PID model-based adaptive robust control method is proposed in order to design a high performance robust controller in the presence of structured (parametric) uncertainties and unstructured uncertainties. The approach improves performance by using the advantages of sliding mode control, adaptive control, and PID controller, while th...

This research describes the design and implementation of nonlinear control strategies for three dimensions joint dental robot manipulators whose dynamic or kinematic models are uncertain. This technique describes the development of an adaptive task-space tracking controller for dental robot manipulators with uncertainty in the kinematic and dynamic...

Recent development of robot technology is revolutionizing the medical field. The concept of using robot assistance in medical surgery has been receiving more and more recognition throughout the world. Robot-assisted surgery has the advantage of reducing surgeons' hand tremor, decreasing post-operative complications, reducing patients' pains, and in...

Recent development of robot technology is revolutionizing the medical field. The concept of using robot assistance in medical surgery has been receiving more and more recognition throughout the world. Robot-assisted surgery has the advantage of reducing surgeons' hand tremor, decreasing post-operative complications, reducing patients' pains, and in...