FDI Scheme Research Articles

Legal Policy Impact on Indonesia's Geothermal Industry Development: FDI Cooperation and NDC

Geothermal significantly contributes to the state's energy industry development as a widely available low-carbon, sustainable, and strategic resource. As Indonesia is committed to achieving NZE 2060, renewable energy development is being conducted massively. Seeking Indonesia's geothermal energy potential, several regulations, policies, and schemes are implemented, including investment schemes, as financing plays a vital role in the industry's development. However, establishing a seamless procurement is highly dependent on national law regulations which govern the financing, procurement, and utilizations up to investors benefits. This article identifies international and national regulations on FDI, business schemes, and its alignment with INDC strategic programs, demonstrating Indonesia's commitment. It finds much room for improvement in national law regulation, especially the upcoming RE Draft Bill, as the implementation of established FDI schemes has not met the investor's favors. However, among AMS, Indonesia is the most committed country to achieving NZE. Therefore this article examines the Legal Policy Impact on Indonesia’s Geothermal Industry Development through FDI Cooperation and NDC. This article is conducted in Normative Legal Research and uses the Statutory Approach as its problem approach.

Measuring The Contribution of Investment, Export and Internet Usage on Welfare: Indonesian Case Study

This study aims to investigate the influence of investment, exports, and the internet usage on people's welfare, as measured by Regional GDP (RGDP) per capita and the Human Development Index (HDI). The estimation technique employed is Fixed Effect Model with path analysis approach applied to 34 provinces in Indonesia for the period of 2016-2020. The finding suggests that local government spending, exports, and internet use have a positive and significant effect on the RGDP per capita of which act as intervening variable, thus those three variables are indirectly affect HDI. Furthermore, RGDP per capita, local government spending, and investment are simultaneously have a positive and significant effect on HDI. However, according to t-statistic result, the impact of FDI is not significant statistically. This is possibly due to the FDI schemes in Indonesia of which constitute as non-project investment, where technology spillover is considered minimum, and capital gains tend to be savored by its beneficial owner in foreign countries.

FAULT DETECTION AND ISOLATION BASED ON HYBRID SLIDING MODE OBSERVER and FUZZY LOGIC

Faults detection and isolation scheme on the bases of a hybrid Sliding Mode Observer (SMO) technique and fuzzy logic technique has been presented in this paper. The SMO technique was used for the fault detection purpose based on residual signal which represent the difference between output of the process and output of the model that used as a fault indicator, while the fuzzy logic technique was used as a fault isolator depending on fuzzy rules base and fuzzy membership functions. The performance of this hybrid technique is evaluated on a model of DC motor, the proposed scheme is illustrated on a Matlab/Simulink simulator and the results demonstrated a successful implementation of the proposed Fault Detection and Isolation FDI scheme.

A New Residual Evaluation Function Based Fault Diagnosis for a Kind of Nonlinear Systems

AbstractIn this paper, a new residual evaluation function (EF) is proposed for the Hi/H∞ fault detection filter (FDF). Residuals are projected into the direction of fault, then energy of this projection is adopted as the new EF. Compared with the traditional EF, improvement in FD ability of the new EF is demonstrated rigorously, and a recursive way to calculate it is also proposed under the assumption that fault is slowly time varying. Furthermore, application of this new EF for fault isolation (FI) is discussed, which finally leads to a systematic FDI scheme for a kind of nonlinear systems. Simulations with a nonlinear unmanned aerial vehicle (UAV) model validate the proposed method.

Super twisting controller-based unified FDI and FTC scheme for air path of diesel engine using the certainty equivalence principle

This paper proposes a combination of higher order sliding mode and adaptive control for unified fault detection and isolation and fault tolerant control (FTC) of the air path of a diesel engine. Current diesel engines are equipped with features such as variable geometry turbochargers (VGT) and exhaust gas recirculation (EGR) for exhaust emission control. Since EGR and VGT systems are present in the exhaust channel, they are strongly coupled and are prone to both structured as well as unstructured faults. The proposed controller detects and estimates the structured faults by means of adaptation laws, designed by making use of the certainty equivalence principle. Fault effects are compensated by repositioning the actuators. This allows relaxation of the boundedness condition of the super twisting algorithm, as sliding mode controller gains are required to dominate the unstructured parts only, which consequently reduces chattering. A nonlinear multi-input multi-output reduced state control-oriented model has been employed for working out the FTC strategy for EGR and VGT actuators. The stability of the overall system has been analysed using the Lyapunov stability criterion. Faults and proposed controllers are simulated using a fully validated industrial scale diesel engine model to establish the effectiveness of the algorithm.

Admissible Model Matching Fault Tolerant Control based on LPV Fault Representation

In this paper, an approach to design an Admissible Model Matching (AMM) Fault Tolerant Control (FTC) based on Linear Parameter Varying (LPV) fault representation is proposed. The main contribution of this approach is to consider parametric faults as a scheduling variable in the LPV fault representation of the system that allows the controller reconfiguration on-line. The proposed strategy is an active technique that requires the fault to be detected, isolated and estimated by a FDI scheme. In case the fault estimation is not available, a passive strategy based on a single AMM FTC controller could be designed. The formulation of AMM is based on the set of admissible behaviors that are characterized by means of Linear Matrix Inequalities (LMIs). The LMIs allow to locate the poles of the close-loop system inside a region of the unit circle and the fault accommodation can be formulated in terms of several LMIs. The effectiveness and performance of proposed approach have been illustrated in simulation considering a thermal hydraulic system.

Reliability assessment method for structural observer based FDI scheme by a graph theoretic approach

The aim of this paper is to assess the reliability of a fault detection and isolation scheme (FDI) and the impact of sensor’s failure probability on such scheme. The proposed method is based on a graph-theoretic approach and assumes only the knowledge of the system’s structure. For a structured linear system (SLS), we first recall the fault diagnosis conditions when using an observer based scheme. Then, we deduce the sets of sensors that ensure the validity of such conditions. Next, we proceed with a reliability analysis of this kind of diagnosability property based on the sensors’ reliability. Through the assessment of an importance factor, we propose a simple maintenance strategy to maintain the level of the property reliability. The contribution concerns the combination of dependability and structural analysis for studying system’s properties.

Web-Based Monitoring and Control of Industrial Processes Used for Control Education

Web-based technologies enable the implementation of remote monitoring and control of industrial plants. In this paper, some solutions and advantages of using distributed process control systems for control education are presented. In the area of process control teaching, practical experience plays an important role and web-based technologies enable the implementation of remote experiments onto real laboratory or industrial systems for effective engineering education. We present our experiences with two remote experiments based on distributed control systems realized on laboratory systems. The user interface of the first experiment is implemented by professional supervision tools and is using web-based SCADA technology. In the second experiment we discuss the process of developing fault detection and isolation applications for online and remote education by using industrial equipment from the field of process technology (batch, continuous) supported by web technologies. Through-out the course, the student can remotely develop and test model-based and data-driven FDI schemes in Matlab/Simulink by using OPC technology. Finally, the improved platform for web-based remote control system experiments suitable for control education is proposed.

An adaptive sliding mode differentiator for actuator oscillatory failure case reconstruction

This paper proposes an adaptive sliding mode super-twisting differentiator which allows the gains to adapt based on the ‘quality’ of the sliding motion. A Lyapunov based analysis for the adaptive super-twisting scheme is presented to demonstrate its properties. As an example, the adaptive differentiator proposed in this paper has been used as part of a nonlinear FDI scheme for an Oscillatory Failure Case (OFC) in an actuator. The FDI scheme requires an estimate of the rod speed which is provided by the adaptive super-twisting differentiator. Due to the conditions in which the actuator operates, normally the differentiator gains are initialised at low values to ensure good rod speed estimation in fault free conditions. However for large amplitude/frequency OFCs, the gains must adapt in order to maintain sliding and provide a good estimation. Simulations on a high fidelity nonlinear aircraft benchmark model have been carried out for both liquid and solid OFCs.

State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

1 Abstract—Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach. The m ost desirable feature of the neural models applied to the FDI and FTC schemes is a small modeling uncertainty, which is defined as a mismatch between the model and the system being considered (3). Therefore, it is important to use an approach reducing the contribution of the structure errors and the parameter estimation inaccuracy of the neural model uncertainty. To solve such a challenging problem, a Group Method of Data Handling (GMDH) neural network (10), (12), (20), (13), (18) have been proposed. The concept of the GM DH approach relies on replacing the complex neural model by the set of the hierarchically connected partial models, which can be chosen with the application of appropriate selection methods. Besides the reduction of the neural m odel inaccuracy it is important to calculate the model uncertainty in the form of mathematical description allowing performing the robust fault diagnosis. In this paper, a new approach to the actuator and sensors fault detection and isolation is proposed. To solve such a challenging problem, the system outputs and inputs estimator based on the dynamic GMDH neural network is employed. In order to achieve this goal a new structure of the dynamic neuron in the state-space representation is proposed. This description enables to obtain constraints of the parameter estimates which warranty the stability of dynamic GMDH neural model. In order to obtain the constrained parameter estimates and the neural model uncertainty description the Unscented Kalman Filter (UKF) (7), (27), (28) was used. This knowledge enables to calculate the output adaptive threshold. Moreover, the methodology of estimation of the GMDH neural model inputs and the calculation of the input adaptive threshold with the application of the Unknown Input Filter (UIF) is proposed. The obtained input and output adaptive thresholds allow to perform the robust fault diagnosis and actuators isolation of the dynamic non-linear systems. The paper consists of six sections. After the introduction, presenting the subject of this paper, in Section II a concept of robust fault detection and isolation of sensors and actuators is presented. Section III presents the process of synthesis of GMDH neural model. In section IV, the estimation of neural model inputs is realized with the UIF. The experimental results of the robust fault diagnosis are shown in section V. Finally, section VI concludes the paper.

Parameter Estimation for Fault Diagnosis in Nonlinear Systems by ANFIS

This paper presents a fault diagnosis approach to detect and estimate components faults in satellite's attitude control systems (ACSs). The proposed solution provides a framework to detect, isolate, and estimate various faults in system components, using Adaptive Fuzzy Inference Systems Parameter Estimators (ANFISPEs) that are designed and based on parameterizations related to each class of fault. Each ANFISPE estimates the corresponding unknown Fault Parameter (FP) that is further used for fault detection, isolation and identification purposes. Simulation results reveal the effectiveness of the developed FDI scheme of an ACSs actuators of a 3-axis stabilized satellite even in presence of disturbances.

Verification & Validation of a Satellite Fault Detection and Isolation Scheme Based on Sliding-Mode Observers

In this article, the verification and validation (V&V) of a fault detection and isolation scheme based on sliding mode observer residual evaluators and threshold-based residual analysis of gyro and thruster faults for the Mars EXpress (MEX) satellite is presented. The results were part of a European Space Agency project with the goal of examining the potential applicability of modern model-based FDI techniques for on-board satellite deployment. The V&V campaign, consisting of firstly a set of specified fault simulations and secondly a Monte Carlo campaign, has been performed using an industrial-level functional engineering simulator developed around a high-fidelity model of the MEX satellite operating during the Sun Acquisition Mode (SAM), which includes up to 6 different controller mode changes. The results show the good performance and robustness of the FDI scheme throughout the SAM phase.

Validation of Sliding Mode Observer FDI Schemes on the ADDSAFE Functional Engineering Simulator

This paper presents evaluation results from using second order sliding mode observer FDI schemes on the ADDSAFE Functional Engineering Simulator. This has provided fault detection and isolation design verification and performance assessment in preparation for the industrial evaluation in DEIMOS. This paper presents design verifications for three ADDSAFE benchmark problems: one sensor fault scenario and two actuator fault scenarios. The performance of each of the FDD schemes is assessed using the FES's pseudo-Monte-Carlo setup, where a predetermined grid or sequence is used to cover a wide range of flight conditions and perturbations in the aircraft parameters. Detection logic and statistical assessment for the three FDD schemes are presented.

A Decoupled Parameters Estimators for in Nonlinear Systems Fault diagnosis by ANFIS

This paper presents a new and efficient Adaptive Neural Fuzzy Inference Systems approach for satellite’s attitude control systems (ACSs) fault diagnosis. The proposed approach formulates the fault modelling problem of system component into an on-line parameters estimation The learning ability of the adaptive neural fuzzy inference system allow as to decoupling the effect of each fault from the estimation of the others. Our solution provides a method to detect, isolate, and estimate various faults in system components, using Adaptive Fuzzy Inference Systems Parameter Estimators (ANFISPEs) that are designed and based on parameterizations related to each class of fault. Each ANFISPE estimates the corresponding unknown Fault Parameter (FP) that is further used for fault detection, isolation and identification purposes. Simulation results reveal the effectiveness of the developed FDI scheme of an ACSs actuators of a 3-axis stabilized satellite. DOI: http://dx.doi.org/10.11591/ijece.v2i2.221

Fault reconstruction using a LPV sliding mode observer for a class of LPV systems

This paper proposes a new sliding mode observer for fault reconstruction, applicable for a class of linear parameter varying (LPV) systems. Observer schemes for actuator and sensor fault reconstruction are presented. For the actuator fault reconstruction scheme, a virtual system comprising the system matrix and a fixed input distribution matrix is used for the design of the observer. The fixed input distribution matrix is instrumental in simplifying the synthesis procedure to create the observer gains to ensure a stable closed-loop reduced order sliding motion. The ‘output error injection signals’ from the observer are used as the basis for reconstructing the fault signals. For the sensor fault observer design, augmenting the LPV system with a filtered version of the faulty measurements allows the sensor fault reconstruction problem to be posed as an actuator fault reconstruction scenario. Simulation tests based on a high-fidelity nonlinear model of a transport aircraft have been used to demonstrate the proposed actuator and sensor FDI schemes. The simulation results show their efficacy.

Observer-based FDI Schemes for Wind Turbine Benchmark

AbstractIn this paper, observer-based FDI schemes for wind turbines are proposed. This study is based on the benchmark model presented in Odgaard et al. [2009a]. For residual generation, Kalman filter and diagnostic observer based approaches are employed, and for residual evaluation, generalized likelihood ratio test and cumulative variance index are chosen. The fault isolation issue is solved with the help of dual sensor redundancy. Finally, the performance of the proposed FDI schemes is systematically evaluated by Monte Carlo studies.

Robust Fault Diagnosis strategies for Spacecraft Application to LISA Pathfinder experiment

AbstractThis paper presents research activities conjointly led by EADS Astrium Satellites and the European Space Agency on innovative and robust health monitoring system for the next generation of spacecraft. Two robust FDI schemes are presented to detect and isolate faults affecting the micro-Newton colloidal thrust system of the LISA Pathfinder spacecraft. The first FDI strategy is based on a bank of eight H∞/H− residual generators designed according to the Generalized Observer Strategy whereas the second strategy consists of Kalman-based projected observers. The efficiency of the proposed FDI techniques is assessed through non linear simulations performed under realistic conditions (physical parameter uncertainties, disturbances, measurement noises, measurement delays, thruster jet misalignment,…). The results are quite encouraging, illustrate the effectiveness of the proposed techniques and suggest that the solutions could be practical viable candidates.

Robust FDI Scheme in Power Actuators for Induction Motor

In this paper a robust model-based FDI scheme for actuator faults in induction motor (IM) drives is presented. A voltage source inverter is studied as power actuator for the induction motor. Residuals are synthesized by applying a differential geometry approach. In order to obtain a robust observer-based FDI scheme a bank of PI-observers is used, which according to the fault time profile allows to identify the faults. Departing from the results in Espinoza et al. [2008], the diagnosability and decoupling conditions for actuator faults are satisfied in the IM model. In this way, residuals are decoupled from the load torque and operating conditions, and simultaneous faults can be addressed. Hence, in order to isolate faults related to switching devices, it is suggested a directional residual evaluation. Hence 3 residuals are constructed to isolate the faulty switches. The ideas presented in the paper are verified experimentally in a test-bench of 1 HP induction motor under single and simultaneous faults.

Neurofuzzy Modelling and Pattern Matching for Online Fault Detection and Isolation of Nonlinear DC Motors

An online fault detection and isolation scheme for nonlinear systems based on neurofuzzy modelling and pattern matching is developed in this paper. The system is first modelled offline by a neurofuzzy network using data obtained under normal operating conditions. Another neurofuzzy network is then used to model the residual, which is the difference between the output of the system and that from the neurofuzzy network. For online fault monitoring, it is necessary to construct first a fault database that contains fuzzy rules for all possible faults in the system. Recursive least squares algorithm is used to train the network online, from which the IF-THEN rules are extracted. Faults are isolated online by comparing these fuzzy rules with those in the fault database using a nearest neighbour classifier. A simulation example involving a nonlinear DC motor control system is used to demonstrate the implementation and performance of the proposed FDI scheme.

Fault diagnosis of an industrial gas turbine prototype using a system identification approach

In this work, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine simulated process is presented. The main point of the paper consists of exploiting an identification scheme in connection with dynamic observer or filter design procedures for diagnostic purposes. Thus, black-box modelling and output estimation approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance achieved. Moreover, the suggested scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to experimentally verify the robustness of the solution obtained, the proposed FDI strategy has been applied to the simulation data of a single-shaft industrial gas turbine plant in the presence of measurement and modelling errors. Hence, extensive simulations of the test-bed process and Monte Carlo analysis are the tools for assessing experimentally the capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods.