Time-varying Operating Conditions Research Articles

Online Stochastic Optimization of Networked Distributed Energy Resources

This paper investigates distributed control and incentive mechanisms to coordinate distributed energy resources (DERs) with both continuous and discrete decision variables as well as device dynamics in distribution grids. We formulate a multiperiod social welfare maximization problem, and based on its convex relaxation propose a distributed stochastic dual gradient algorithm for managing DERs. We further extend it to an online real-time setting with time-varying operating conditions, asynchronous updates by devices, and feedback being leveraged to account for nonlinear power flows as well as reduce communication overhead. The resulting algorithm provides a general online stochastic optimization algorithm for coordinating networked DERs with discrete power setpoints and dynamics to meet operational and economic objectives and constraints. We characterize the convergence of the algorithm analytically and evaluate its performance numerically.

A Simple Condition Monitoring Method for Gearboxes Operating in Impulsive Environments.

Reliable condition indicators are necessary to perform effective diagnosis and prognosis. However, the vibration signals are often corrupted with non-Gaussian noise and rotating machines may operate under time-varying operating conditions. This impedes the application of conventional condition indicators. The synchronous average of the squared envelope is a relatively simple yet effective method to perform fault detection, fault identification and fault trending under constant and time-varying operating conditions. However, its performance is impeded by the presence of impulsive signal components attributed to impulsive noise or the presence of other damage modes in the machine. In this work, it is proposed that the synchronous median of the squared envelope should be used instead of the synchronous average of the squared envelope for gearbox fault diagnosis. It is shown on numerical and experimental datasets that the synchronous median is more robust to the presence of impulsive signal components and is therefore more reliable for estimating the condition of specific machine components.

A methodology for identifying information rich frequency bands for diagnostics of mechanical components-of-interest under time-varying operating conditions

Performing condition monitoring on rotating machines such as wind turbines, which operate inherently under time-varying operating conditions, remains a challenge. The signal components generated by incipient damage are masked by other signal components that are not of interest and high noise levels. In this work, a new method, referred to as the IFBIαgram, is proposed that is capable of identifying frequency bands that are rich with diagnostic information related to specific cyclic components. This allows the optimal frequency band to be determined for diagnosing the component-of-interest. It is shown on numerical and experimental gearbox data that this method is not only capable of detecting incipient damage, but is also robust to time-varying operating conditions. Therefore, it can be used to independently determine the condition of different mechanical components and it is robust to spurious transients.

Online Detection of Events With Low-Quality Synchrophasor Measurements Based on $i$Forest

In this article, we propose an online data-driven approach that leverages the isolation mechanism for fast event detection with low-quality data measurement. The proposed adaptive and online isolation forest ( $i$ Forest)-based detection (AOIFD) method adopts a hierarchical subspace feature selection scheme to design two levels of detectors. As such, it is capable of differentiating events from low-quality data measurements, preventing false alarms in the presence of low-quality data measurements. We further propose a data augmentation method to address the training data imbalance, which is caused by the rare occurrence of events. Moreover, we propose an adaptive training process to update the AOIFD method so that it can adapt to the time-varying operating conditions of power systems. The proposed AOIFD algorithm is practical in the sense that it is a fast-response method that requires no system modeling information and no global communications. Case studies with both synthetic and realistic PMU data are conducted to validate the effectiveness of the proposed method.

Real-Time Prediction for IC Aging Based on Machine Learning

Estimating the aging-related degradation and failure of nanoscale integrated circuits (ICs), before they actually occur, is crucial for developing aging prevention/mitigation actions and in turn avoiding unexpected in-field circuit failures. Real-time monitoring of IC operating conditions can be efficiently used for predicting aging degradation and in turn timing failures caused by device aging. The existing approaches only take some specific operating conditions (e.g., workload or temperature) into account. In this paper, we propose a novel method for real-time IC aging prediction by extending the prediction schemes to a comprehensive model which takes into account any time-variant dynamic operating conditions relevant to aging prediction. Using a machine learning prediction model and the notion of equivalent aging time, we show that our approach outperforms the existing methods in terms of aging-prediction accuracy under different scenarios of time-variant operating conditions.

Fast Nonlinear Chirplet Dictionary-Based Sparse Decomposition for Rotating Machinery Fault Diagnosis Under Nonstationary Conditions

Fault diagnosis under time-varying operational conditions is always the challenge in industrial systems. The chirplet transform (CT) provides an effective first-order approximation approach, but the slow operation speed and the drawback for high-order signal analysis limit its uses. Focusing on this problem, a fast nonlinear chirplet dictionary-based sparse decomposition (FNC-SD) method for nonlinear signal analysis is proposed. By replacing the time–frequency inclination parameter in the CT with a frequency bending parameter, the proposed method can track the nonstationary signals by arbitrary order polynomial law with time by an additional degree. Considering that too many parameters will slow down the operation speed, a frequency bending operator estimation algorithm is also proposed in this paper based on the calculated rotating frequency. Therefore, a laser vibrometer is needed in the experiment to collect the impulse signals, which can represent rotating speed signals. Then, the FNC-SD method is used to construct a redundant dictionary for signal sparse time–frequency representation. The high similarity between signals and atoms can make the method prefer to choose the right atoms, instead of being influenced by noise. Therefore, the proposed method also shows a strong noise robustness. Two simulation experiments are constructed to verify the performance of the proposed method. Finally, the FNC-SD method is used to diagnose a faulty motor with broken bar. The analyzed results and comparisons with respect to the state-of-the-art methods are illustrated in detail, as well as the convergence accuracy and speed, which highlight the superiority of the proposed method.

Dynamic preventive maintenance strategy for MMC considering multi-term thermal cycles

With the integration of large-scale renewable energy, preventive maintenance (PM) of modular multi-level converter (MMC) has become a critical technique for guaranteeing the secure operation of long-distance transmission systems. However, existing PM strategies result in errors and extra costs because of ignoring the time-varying operational conditions (e.g. wind speed, air temperature and power current alternating) and constant redundant sub-modules (SM). To reduce PM-related costs with an acceptable level of reliability considering different operational conditions, a dynamical PM strategy is proposed. Compared with the traditional PM strategy, adjustable redundant SMs in every operational stage and PM interval are added to dynamically coordinate reliability and PM-related costs of MMC. The reliability model of MMC proposed in this paper considers the impact of different operational conditions based on multi-term thermal cycles of components and operating characteristics of MMC. Monte Carlo method is used to estimate the reliability of MMC, and the genetic algorithm is applied to optimize the PM interval and the number of redundant SM in every operational stage. Case studies show that the PM results vary with different operational conditions. Besides, compared with existing strategies, the proposed dynamic PM strategy could reduce the PM-related costs with an acceptable level of reliability by dynamically adjusting the PM interval and redundancy on every stage.

A methodology using the spectral coherence and healthy historical data to perform gearbox fault diagnosis under varying operating conditions

Condition monitoring is usually performed over long periods of time when critical rotating machines such as wind turbine gearboxes are monitored. There are many potential signal processing and analysis techniques that can be utilised to diagnose the machine from the condition monitoring data, however, they seldom incorporate the available healthy historical data of a machine systematically in the fault diagnosis process. Hence, a methodology is proposed in this article which supplements the order-frequency spectral coherence with historical data from a healthy machine to perform automatic fault detection, automatic fault localisation and fault trending. This has the benefit that the order-frequency spectral coherence, a very powerful technique for rotating machine fault diagnosis under varying speed conditions, can be utilised without requiring an expert to interpret the results. In this methodology, an extended version of the improved envelope spectrum is utilised to extract features from the order-frequency spectral coherence, whereafter a probabilistic model is carefully used to calculate a diagnostic metric for automatic fault detection and localisation. The methodology is investigated on numerical gearbox data as well as experimental gearbox data, both acquired under time-varying operating conditions with two probabilistic models, namely a Gaussian model and a kernel density estimator, compared as well. The results indicate the potential of this methodology for performing gearbox fault diagnosis under varying operating conditions.

Normalisation of the amplitude modulation caused by time-varying operating conditions for condition monitoring

Performing condition monitoring under time-varying operating conditions is challenging. The time-varying operating conditions result in amplitude and frequency modulation which mask the presence of incipient damage and make it difficult to distinguish between changes in the condition of the machine and changes in its operating conditions. In this work, the benefits of normalising the amplitude modulation caused by the varying operating conditions for condition monitoring are illustrated and a method is proposed to perform this normalisation. It is shown that the proposed method can be used as a pre-processing methodology for deterministic-random separation, it can be used to detect incipient damage and it can be used to reliably estimate the severity of the damage under time-varying operating conditions as well. The proposed method is investigated on numerical gearbox data and experimental gearbox data, where its benefits for condition monitoring under time-varying operating conditions are shown.

Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis

Abstract Condition monitoring signals obtained from rotating machines often demonstrate a highly non-stationary and transient nature due to internal natural deterioration characteristics of their constituent components and external time-varying operational conditions. Traditional multivariate statistical monitoring approaches are based on the assumption that the underlying processes are linear and static and are apt to interpret the normal changes in operating conditions as faults, which would result in high false positive rates. On the other hand, the development of robust diagnostic techniques for the detection of incipient faults remains a challenge for researchers, given the difficulty of finding an appropriate trade-off between a low false positive ratio and early detection of emerging faults. To address these issues, this paper proposes a novel adaptive fault detection approach based on the canonical residuals (CR) induced by the combination of canonical variate analysis (CVA) and matrix perturbation theory for the monitoring of dynamic processes where variations in operating conditions are incurred. The canonical residuals are calculated based upon the distinctions between past and future measurements and are able to effectively detect emerging faults while still maintaining a low false positive rate. The effectiveness of the developed diagnostic model for the detection of abnormalities in industrial processes was demonstrated for slow involving faults in case studies of two operational industrial high-pressure pumps. In comparison with the variable-based and canonical correlation-based statistical monitoring approaches, the proposed canonical residuals-adaptive canonical variate analysis (CR-ACVA) fault detection method has demonstrated its superiorities by the detailed performance comparisons.

Introduction to the benchmark challenge on common rail pressure control of gasoline direct injection engines

As one of the most important actuators for gasoline direct injection technology, common rail systems provide the requested rail pressure for fuel injection. Special system characteristics, such as coupled discrete-continuous dynamic in the common rail system, limited measurable states, and time-varying engine operating conditions, impel the combination of advanced methods to obtain the desired injection pressure. Therefore, reducing the pressure fluctuation and satisfying engineering implementation have become noteworthy issues for rail pressure control (RPC) systems. In this study, the benchmark problem and the design specification of RPC proposed by 2018 IFAC E-CoSM Committee are introduced. Moreover, a common rail system model is provided to the challengers, and a traditional PI control is applied to show the problem behaviors. Finally, intermediate results of the challengers are summarized briefly.

Control-oriented dynamic model of an inductively coupled plasma torch by artificial intelligence methodology

Inductively coupled plasma (ICP) torches have been widely used in various materials processing. To improve the control of their processes, there has been a growing demand for simplified numerical models which can rapidly predict the dynamics of plasma jets subject to time-varying inputs or external disturbances. In this paper, control-oriented dynamic models of an ICP torch are developed as an alternative to the complex, high-level 2D time-dependent numerical model (i.e. a model based on magneto-hydrodynamic equations). Prior to model development, the detailed dynamic and nonlinear nature of the ICP torch to its time-varying operation conditions was numerically investigated using a 2D numerical model to gain insight into choosing appropriate dynamic model structures. Linear ARX (AutoRegressive with eXogenous input) and ARX-type neural network models were selected in the model identification, and their parameters or weightings were determined using the input/output data obtained from the 2D time-dependent numerical model. The dynamic behaviours of the ICP torch predicted from the developed models were in good agreement with the data from the 2D time-dependent numerical model. Using the developed models, a simple control system for the plasma temperature and axial velocity regulations was also designed and tested. The feedback control simulations demonstrated good set point tracking and disturbance rejection performances, indicating that the developed approach can be directly applied to the model-based control system design of an ICP torch as well as other thermal plasma torches.

Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model

The growth of the Industrial Internet of Things (IIoT) has generated a renewed emphasis on research of prognostic degradation modeling whereby degradation signals, such as vibration signals, temperature and acoustic emissions, are used to estimate the state-of-health and predict the remaining useful life (RUL). Besides the inherent system state, external operating conditions, such as the rotational speed and load also play a significant role in the behavior of degradation signals. Time-varying operating conditions often cause two major effects on the degradation signals. First, they change the degradation rate of systems. Second, they cause signal jumps at condition change-points. These two factors make RUL prediction more difficult under time-varying operating conditions. This paper proposes a RUL prediction method by introducing these two factors into a state-space model. Changes in the degradation rate are introduced into a state transition function, and jumps in the degradation signals are introduced into a measurement function. The separate analysis of these two factors makes it possible to distinguish their own contributions to RUL prediction, thus avoiding false alarms and improving the prediction accuracy. The effectiveness of the proposed method is demonstrated using both a simulation study and an accelerated degradation test of rolling element bearings.

Transient simulation of ammonia–water mixture desorption for absorption heat pumps

Prediction of transient response is crucial to understanding the impact of time-varying operating conditions and the development of field-deployable control systems. A mathematical framework to simulate the transient response of a desorber for small-capacity ammonia–water absorption heat pumps is presented. The model is based on dynamic analysis of conservation equations and accounts for the thermal capacitances in the heat exchanger wall material and fluid volumes. The numerical solver and its implementation are presented. Improved computational performance is achieved by using advanced solvers for stiff differential-algebraic equations. The steady-state predictions of component performance from the model are experimentally validated using a microscale ammonia–water desorber. Transient performance of the desorber in response to changes in the solution flow rate, heat source temperature and ambient conditions is accurately predicted with the effects of mass and thermal energy storage. Transient studies are used to identify control strategies for the desorber component and the overall absorption system.

Helicopter gearbox vibration fault classification using order tracking method and genetic algorithm

ABSTRACTIn this paper, we implemented a diagnostic system for vibration faults that occur on the PUMA helicopter gearbox. We used an approach based on the joint use of the Order Tracking signal analysis and the Genetic Algorithm. To achieve this goal, we first collected a database of vibration signals measured during periodic inspections. The available vibration signals are acquired under a time-varying operating conditions. Therefore, we used the Order Tracking method, which is more accurate in extracting faults features. This technique was performed by resampling the vibration data and then applying the Short Time Fourier Transform. To enable efficient and continuous monitoring of gearbox vibration faults from features, we used Genetic Algorithm to build a rules-based diagnostic model. Genetic operators have been adapted to the specificity of the problem to optimize the parameters of this model. This approach is successfully applied to the diagnosis of vibration defects of helicopter gearboxes. The results have been validated effectively with test data. The diagnostic model can therefore be implemented on helicopter computers to detect faults in flight or on the ground. This approach has been used for the first time in the field of helicopter gearbox vibration fault diagnosis.

Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions

Fault diagnosis of wind turbine (WT) gearboxes can reduce unexpected downtime and maintenance costs. In this paper, a new fault diagnosis framework is proposed based on deep bi-directional Long Short-term Memory (DB-LSTM). Even though deep learning has been used in fault diagnosis of rotating machines, deep learning diagnosis models with the input of raw time-series or frequency data face computational challenges. Additionally, the deviation between datasets can be triggered easily by operating condition variation, which will highly reduce the performance of fault diagnosis models. However, in most studies, several constant operating conditions (e.g., selected some rotational speeds and loads) are used in the experiments, which may not reflect time-varying non-stationary operating conditions of WT gearbox and cannot be applicable in real-life applications. In this work, the experiments are designed that the real rotor speed of WT spindle input to the WT drivetrain test rig to simulate the actual time-varying non-stationary operating conditions. Ten common time-domain features are all fed into the DB-LSTM network to construct fault diagnosis model, which eliminates the need for selecting suitable features manually and improves training time. Vibration data collected by three accelerometers are used to validate the effectiveness and feasibility of the proposed method. The proposed method is also compared with four existing diagnosis models, and the results are discussed.

Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification

In modern digital manufacturing, nearly 79.6% of the downtime of a machine tool is caused by its mechanical failures. Predictive maintenance (PdM) is a useful way to minimize the machine downtime and the associated costs. One of the challenges with PdM is early fault detection under time-varying operational conditions, which means mining sensitive fault features from condition signals in long-term running. However, fault features are often weakened and disturbed by the time-varying harmonics and noise during a machining process. Existing analysis methods of these complex and diverse data are inefficient and time-consuming. This paper proposes a novel method for early fault detection under time-varying conditions. In this study, a deep learning model is constructed to automatically select the impulse responses from the vibration signals in long-term running of 288 days. Then, dynamic properties are identified from the selected impulse responses to detect the early mechanical fault under time-varying conditions. Compared to traditional methods, the experimental results in this paper have proved that our method was not affected by time-varying conditions and showed considerable potential for early fault detection in manufacturing.

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts: fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.

Distributed Voltage Control in Distribution Networks: Online and Robust Implementations

Voltage regulation in distribution networks is increasingly challenged by the penetration of distributed energy resources (DERs). This paper develops a distributed voltage control (DVC) scheme to achieve the globally optimal settings of reactive power provided by DERs’ power electronic interfaces. Based on unbalanced network flow modeling, the alternating direction method of multipliers algorithm is used to develop the proposed distributed design. To simplify the communication network, the power flow coupling is linearized to involve only neighboring buses. Interestingly, this linear approximation model enjoys a satisfactory performance and allows to exchange information only between neighboring buses. The DVC design is further improved for online implementations that can efficiently adapt to time-varying operating conditions. Last, to cope with cyber resource constraints, we slightly change the DVC design using a “freezing strategy” for unavailable variables due to link failures with guaranteed convergence. Numerical simulations using realistic multi-phase feeders and daily generation/load time-series have validated our analytical results and demonstrated the performance improved by the proposed DVC designs.

A complete model for dynamic simulation of a 1-kW class beta-type Stirling engine with rhombic-drive mechanism

This study is aimed at development of a theoretical model by combining modified non-ideal adiabatic model and dynamic analyses in order to predict the dynamic behavior of a 1-kW class beta-type Stirling engine with rhombic drive mechanism during starting. All friction losses caused by piston rings, bearings, and seals are taken into consideration. The total torque can be expressed in a closed-form relation by including the effects of the inertia force, the gravity force, the pressure force and the friction forces. The transient variations of instantaneous angular velocity under different operating conditions are simulated. The variations of the engine speed, the shaft power, and the mechanical efficiency of the engine under time-varying output torque are also described. The results also indicate that engine has a minimum and a maximum operating speed, and a minimum initial speed for starting. A prototype engine is built and tested for validation of the present model. Experiments on the transient behavior of the prototype engine during starting and under time-varying operating conditions are conducted. The numerical predictions by the present model closely agree with the experiments. And the maximum power generated by the engine is 1358 W at 1313 rpm in the test.