Introduction. Constant increases in power consumption by both industrial and individual users may cause depletion of fossil fuels and environmental pollution, and hence there is a growing interest in clean and renewable energy resources. Photovoltaic power generation systems are playing an important role as a clean power electricity source in meeting future electricity demands. 

Problem. All photovoltaic systems have two problems; the first one being the very low electric-power generation efficiency, especially under low-irradiation states; the second resides in the interdependence of the amount of the electric power generated by solar arrays and the ever changing weather conditions. Load mismatch can occur under these weather varying conditions such that maximum power is not extracted and delivered to the load. This issue constitutes the so-called maximum power point tracking problem.

 Aim. Many methods have been developed to determine the maximum power point under all conditions. There are various methods, in most of them based on the well-known principle of perturb and observe. In this method, the operating point oscillates at a certain amplitude, no matter whether the maximum power point is reached or not. That is, this oscillation remains even in the steady state after reaching the maximum power point, which leads to power loss. This is an essential drawback of the previous method. In this paper, a cascade sliding mode maximum power point tracking control for a photovoltaic system is proposed to overcome above mentioned problems. 

Methodology. The photovoltaic system is mainly composed of a solar array, DC/DC boost converter, cascade sliding mode controller, and an output load. Two sliding mode control design strategies are joined to construct the proposed controller. The primary sliding mode algorithm is designed for maximum power point searching, i.e., to track the output reference voltage of the solar array. This voltage is used to manipulate the setpoint of the secondary sliding mode controller, which is used via the DC-DC boost converter to achieve maximum power output. 

Results. This novel approach provides a good transient response, a low tracking error and a very fast reaction against the solar radiation and photovoltaic cell temperature variations. The simulation results demonstrate the effectiveness of the proposed approach in the presence of environmental disturbances.

Introduction. Constant increases in power consumption by both industrial and individual users may cause depletion of fossil fuels and environmental pollution, and hence there is a growing interest in clean and renewable energy resources. Photovoltaic power generation systems are playing an important role as a clean power electricity source in meeting future electricity demands. 

Problem. All photovoltaic systems have two problems; the first one being the very low electric-power generation efficiency, especially under low-irradiation states; the second resides in the interdependence of the amount of the electric power generated by solar arrays and the ever changing weather conditions. Load mismatch can occur under these weather varying conditions such that maximum power is not extracted and delivered to the load. This issue constitutes the so-called maximum power point tracking problem.

 Aim. Many methods have been developed to determine the maximum power point under all conditions. There are various methods, in most of them based on the well-known principle of perturb and observe. In this method, the operating point oscillates at a certain amplitude, no matter whether the maximum power point is reached or not. That is, this oscillation remains even in the steady state after reaching the maximum power point, which leads to power loss. This is an essential drawback of the previous method. In this paper, a cascade sliding mode maximum power point tracking control for a photovoltaic system is proposed to overcome above mentioned problems. 

Methodology. The photovoltaic system is mainly composed of a solar array, DC/DC boost converter, cascade sliding mode controller, and an output load. Two sliding mode control design strategies are joined to construct the proposed controller. The primary sliding mode algorithm is designed for maximum power point searching, i.e., to track the output reference voltage of the solar array. This voltage is used to manipulate the setpoint of the secondary sliding mode controller, which is used via the DC-DC boost converter to achieve maximum power output. 

Results. This novel approach provides a good transient response, a low tracking error and a very fast reaction against the solar radiation and photovoltaic cell temperature variations. The simulation results demonstrate the effectiveness of the proposed approach in the presence of environmental disturbances.

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems.

Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. 

Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. 

Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems.

Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. 

Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. 

Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems.

Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. 

Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. 

Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems.

Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. 

Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. 

Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

This article deals with the problem of blind separation of statistically independent sources from the instantaneous linear model (n × n). When the observation signals are affected by the additive white gaussian noise (AWGN), the implementation of the proposed solution is performed by following three steps. The first step is a whitening process. The second step aims to convert the uncorrelated signals into statistically independent signals. The last step consists in reducing the noise existing in the noisy estimations. The main part of the proposed solution is to determine the adequate rotating angle (θ) that maximizes the kurtosis of the whitened signals. This rotating angle is obtained through the use of optimization techniques by applying a genetic algorithm. The proposed solution has the advantage of not converging to a local maximum, and also the separation method can be easily generalized to converge directly towards the global maximum for the case of several sources. The results obtained by applying many simulations, prove the effectiveness and the performance of the proposed method even in the noisy case and whatever the type of the signals (stationary or non-stationary).

This article deals with the problem of blind separation of statistically independent sources from the instantaneous linear model (n × n). When the observation signals are affected by the additive white gaussian noise (AWGN), the implementation of the proposed solution is performed by following three steps. The first step is a whitening process. The second step aims to convert the uncorrelated signals into statistically independent signals. The last step consists in reducing the noise existing in the noisy estimations. The main part of the proposed solution is to determine the adequate rotating angle (θ) that maximizes the kurtosis of the whitened signals. This rotating angle is obtained through the use of optimization techniques by applying a genetic algorithm. The proposed solution has the advantage of not converging to a local maximum, and also the separation method can be easily generalized to converge directly towards the global maximum for the case of several sources. The results obtained by applying many simulations, prove the effectiveness and the performance of the proposed method even in the noisy case and whatever the type of the signals (stationary or non-stationary).

This paper deals with the design and optimization of a 1.2 MVA medium-voltage (MV) power electronic traction transformer (PETT) for an AC 15 kV/16.7 Hz railway grid, in which a simple two-stage multi-cell PETT topology consisting of a bidirectional 170 kW, 2.5 kV AC rms to 6 kV DC power factor corrected (PFC) converter stage followed by a bidirectional isolated 46 kHz, 6 kV to 1.5 kV series resonant DC/DC converter for each cell is presented. This paper presents a methodology that maximizes the converter"s efficiency and minimizes the converter"s size and weight. Accordingly, the first stage employs 10 kV SiC MOSFETs based on the integrated Triangular Current Mode (iTCM). The second stage uses 10 kV SiC MOSFETs on the MV-side, 3.3 kV SiC MOSFETs on the LV-side, and a medium frequency (MF) MV transformer operating at 46 kHz. MF transformers offer a way to reduce weight and improve energy efficiency, particularly in electric multiple-unit applications. The MF MV transformer requires power electronic converters, which invert and rectify the voltages and currents at the desired operating frequency. The development of high voltage SiC MOSFETs, which can be used instead of Si IGBTs in PETT topologies, increases the operating frequency without reducing the converter"s efficiency. The designed MV PETT achieves 98.95% efficiency and 0.76 kVA/kg power density.

This paper deals with the design and optimization of a 1.2 MVA medium-voltage (MV) power electronic traction transformer (PETT) for an AC 15 kV/16.7 Hz railway grid, in which a simple two-stage multi-cell PETT topology consisting of a bidirectional 170 kW, 2.5 kV AC rms to 6 kV DC power factor corrected (PFC) converter stage followed by a bidirectional isolated 46 kHz, 6 kV to 1.5 kV series resonant DC/DC converter for each cell is presented. This paper presents a methodology that maximizes the converter"s efficiency and minimizes the converter"s size and weight. Accordingly, the first stage employs 10 kV SiC MOSFETs based on the integrated Triangular Current Mode (iTCM). The second stage uses 10 kV SiC MOSFETs on the MV-side, 3.3 kV SiC MOSFETs on the LV-side, and a medium frequency (MF) MV transformer operating at 46 kHz. MF transformers offer a way to reduce weight and improve energy efficiency, particularly in electric multiple-unit applications. The MF MV transformer requires power electronic converters, which invert and rectify the voltages and currents at the desired operating frequency. The development of high voltage SiC MOSFETs, which can be used instead of Si IGBTs in PETT topologies, increases the operating frequency without reducing the converter"s efficiency. The designed MV PETT achieves 98.95% efficiency and 0.76 kVA/kg power density.

This paper deals with the design and optimization of a 1.2 MVA medium-voltage (MV) power electronic traction transformer (PETT) for an AC 15 kV/16.7 Hz railway grid, in which a simple two-stage multi-cell PETT topology consisting of a bidirectional 170 kW, 2.5 kV AC rms to 6 kV DC power factor corrected (PFC) converter stage followed by a bidirectional isolated 46 kHz, 6 kV to 1.5 kV series resonant DC/DC converter for each cell is presented. This paper presents a methodology that maximizes the converter"s efficiency and minimizes the converter"s size and weight. Accordingly, the first stage employs 10 kV SiC MOSFETs based on the integrated Triangular Current Mode (iTCM). The second stage uses 10 kV SiC MOSFETs on the MV-side, 3.3 kV SiC MOSFETs on the LV-side, and a medium frequency (MF) MV transformer operating at 46 kHz. MF transformers offer a way to reduce weight and improve energy efficiency, particularly in electric multiple-unit applications. The MF MV transformer requires power electronic converters, which invert and rectify the voltages and currents at the desired operating frequency. The development of high voltage SiC MOSFETs, which can be used instead of Si IGBTs in PETT topologies, increases the operating frequency without reducing the converter"s efficiency. The designed MV PETT achieves 98.95% efficiency and 0.76 kVA/kg power density.

The growth of manufacturing industries and the huge competitive environment forced manufacturing organizations to develop advanced improvement strategies and enhance their sustainability performance. The integration of sustainable Manufacturing in industrial operations leads to enhanced process performances through the reduction of wastes, cost, and environmental impacts and satisfies ergonomic conditions. For this reason, various firms have adopted sustainable manufacturing concepts to enhance their performances and hold a prestigious competitive position. The purpose of this research is to develop an integrated Pythagorean Fuzzy MCDM model to enhance the application process of the conventional Lean Manufacturing approach (LM). Firstly, an extended Value Steam Mapping is proposed to assess the sustainability of the manufacturing process and identify the causes of waste from a sustainability viewpoint. Secondly, Pythagorean Fuzzy Decision-Making Trial And Evaluation Laboratory (PF-DEMATEL) is employed to analyze the interrelationship among the identified. Thirdly, Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS) is introduced to prioritize a set of solutions in order to overcome the investigated causes and improve the durability of the manufacturing operations. Finally, sensitivity analysis is conduced to assess the effectiveness of the obtained results. The proposed method has several attractive features. It can address the drawbacks of the conventional LM and enhance its analysis and improvement tasks. However, the proposed approach offers an advanced application process for Lean Manufacturing in a sustainability context. Additionally, the suggested strategy facilitates the leaders to assess the current state of the manufacturing processes and select the appropriate solutions for successful sustainability implementation. The validity of the proposed approach was investigated in a real case study. The results confirm its effectiveness and indicate that using MCDM approaches in LM application process offers a consistent and flexible demarche for sustainable manufacturing implementation.

The growth of manufacturing industries and the huge competitive environment forced manufacturing organizations to develop advanced improvement strategies and enhance their sustainability performance. The integration of sustainable Manufacturing in industrial operations leads to enhanced process performances through the reduction of wastes, cost, and environmental impacts and satisfies ergonomic conditions. For this reason, various firms have adopted sustainable manufacturing concepts to enhance their performances and hold a prestigious competitive position. The purpose of this research is to develop an integrated Pythagorean Fuzzy MCDM model to enhance the application process of the conventional Lean Manufacturing approach (LM). Firstly, an extended Value Steam Mapping is proposed to assess the sustainability of the manufacturing process and identify the causes of waste from a sustainability viewpoint. Secondly, Pythagorean Fuzzy Decision-Making Trial And Evaluation Laboratory (PF-DEMATEL) is employed to analyze the interrelationship among the identified. Thirdly, Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS) is introduced to prioritize a set of solutions in order to overcome the investigated causes and improve the durability of the manufacturing operations. Finally, sensitivity analysis is conduced to assess the effectiveness of the obtained results. The proposed method has several attractive features. It can address the drawbacks of the conventional LM and enhance its analysis and improvement tasks. However, the proposed approach offers an advanced application process for Lean Manufacturing in a sustainability context. Additionally, the suggested strategy facilitates the leaders to assess the current state of the manufacturing processes and select the appropriate solutions for successful sustainability implementation. The validity of the proposed approach was investigated in a real case study. The results confirm its effectiveness and indicate that using MCDM approaches in LM application process offers a consistent and flexible demarche for sustainable manufacturing implementation.

The growth of manufacturing industries and the huge competitive environment forced manufacturing organizations to develop advanced improvement strategies and enhance their sustainability performance. The integration of sustainable Manufacturing in industrial operations leads to enhanced process performances through the reduction of wastes, cost, and environmental impacts and satisfies ergonomic conditions. For this reason, various firms have adopted sustainable manufacturing concepts to enhance their performances and hold a prestigious competitive position. The purpose of this research is to develop an integrated Pythagorean Fuzzy MCDM model to enhance the application process of the conventional Lean Manufacturing approach (LM). Firstly, an extended Value Steam Mapping is proposed to assess the sustainability of the manufacturing process and identify the causes of waste from a sustainability viewpoint. Secondly, Pythagorean Fuzzy Decision-Making Trial And Evaluation Laboratory (PF-DEMATEL) is employed to analyze the interrelationship among the identified. Thirdly, Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS) is introduced to prioritize a set of solutions in order to overcome the investigated causes and improve the durability of the manufacturing operations. Finally, sensitivity analysis is conduced to assess the effectiveness of the obtained results. The proposed method has several attractive features. It can address the drawbacks of the conventional LM and enhance its analysis and improvement tasks. However, the proposed approach offers an advanced application process for Lean Manufacturing in a sustainability context. Additionally, the suggested strategy facilitates the leaders to assess the current state of the manufacturing processes and select the appropriate solutions for successful sustainability implementation. The validity of the proposed approach was investigated in a real case study. The results confirm its effectiveness and indicate that using MCDM approaches in LM application process offers a consistent and flexible demarche for sustainable manufacturing implementation.

The growth of manufacturing industries and the huge competitive environment forced manufacturing organizations to develop advanced improvement strategies and enhance their sustainability performance. The integration of sustainable Manufacturing in industrial operations leads to enhanced process performances through the reduction of wastes, cost, and environmental impacts and satisfies ergonomic conditions. For this reason, various firms have adopted sustainable manufacturing concepts to enhance their performances and hold a prestigious competitive position. The purpose of this research is to develop an integrated Pythagorean Fuzzy MCDM model to enhance the application process of the conventional Lean Manufacturing approach (LM). Firstly, an extended Value Steam Mapping is proposed to assess the sustainability of the manufacturing process and identify the causes of waste from a sustainability viewpoint. Secondly, Pythagorean Fuzzy Decision-Making Trial And Evaluation Laboratory (PF-DEMATEL) is employed to analyze the interrelationship among the identified. Thirdly, Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS) is introduced to prioritize a set of solutions in order to overcome the investigated causes and improve the durability of the manufacturing operations. Finally, sensitivity analysis is conduced to assess the effectiveness of the obtained results. The proposed method has several attractive features. It can address the drawbacks of the conventional LM and enhance its analysis and improvement tasks. However, the proposed approach offers an advanced application process for Lean Manufacturing in a sustainability context. Additionally, the suggested strategy facilitates the leaders to assess the current state of the manufacturing processes and select the appropriate solutions for successful sustainability implementation. The validity of the proposed approach was investigated in a real case study. The results confirm its effectiveness and indicate that using MCDM approaches in LM application process offers a consistent and flexible demarche for sustainable manufacturing implementation.

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.