Time-varying Operating Conditions Research Articles

An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions

An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions

Asynchronous deep reinforcement learning with gradient sharing for State of Charge balancing of multiple batteries in cyber–physical electric vehicles

This work targets the State of Charge (SoC) imbalance issue due to the mismatch across multiple batteries in cyber–physical electric vehicles (EVs) arisen from a variety of practical factors such as manufacturing tolerance, nonuniform aging process and uneven operation temperature. While most of existing SoC balancing approaches are model-based and require accurate prior knowledge, a data-driven asynchronous deep reinforcement learning (DRL) with gradient sharing is proposed to equilibrate the SoC of multiple battery packs in cyber–physical EVs under time-varying operating conditions, and the transmission of control signals in the EVs is formulated by ISO/IEC 15118 standards to ensure the security of the cyber–physical EVs. The gradient sharing (GS) based exploration scheme can expand the exploration space to diversify the actor policies in the training process and in turn reduce the entropy loss value and guarantee the convergence to the optimal policy in the long term. The proposed asynchronous advantage actor-critic with gradient sharing (A3C-GS) based SoC balancing approach trains the agents based on the output data of the multiple bidirectional DC–DC converter buck-boost circuit to avoid the use of vehicle dynamics and cumbersome operating modes that are difficult to accurately model. Furthermore, the DC bus voltage regulation is integrated simultaneously with the SoC balancing scheme. Comparison results based on simulating three/six batteries based EV system show that the proposed A3C-GS based SoC balancing scheme can achieve the highest steady-state rewards while the oscillation in the early training stage is larger resulted from a larger exploration range.

Degradation modeling under time-varying operating conditions: Inference and prognosis with particle filter

The health state estimation and remaining useful life prediction have always been challenging when a mechanical system works under time-varying operating conditions (TVOC). TVOC can not only influence the amplitude of degradation growth, but also have an impact on how the baseline degradation process is observed. In this paper, we propose a state-space model to estimate the health state of a single-unit system under continuous TVOC. Compared to most existing works where only one discrete operating condition is considered, our model accounts for multi-dimensional and continuous TVOC. The baseline (hidden) degradation is described by stochastic processes or general path models, and the observations are subject to TVOC-related observation bias and noise. Given the raw observations and the TVOC sequences, a particle filter (PF) is used to estimate the hidden degradation process. When the model parameters are unknown, Monte Carlo Expectation-Maximization is used for parameter estimation. As a result, the health state at any time and any TVOC can be estimated. The proposed method is applied to a real choke valve erosion dataset, showing a significant improvement in the health state estimation compared to traditional methods.

Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization

Gas turbine vibration data may exhibit considerable differences under time-varying conditions, which poses challenges for neural network anomaly detection. We first propose a framework for a gas turbine vibration frequency spectra process under time-varying operation conditions, assisting neural networks’ ability to capture weak information. The framework involves scaling spectra for aligning all frequency components related to rotational speed and normalizing frequency amplitude in a self-adaptive way. Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. Finally, a dataset of blade Foreign Object Damage (FOD) fault occurring under time-varying operating conditions was used to validate the framework and anomaly detection. The results demonstrate that the proposed method can effectively reduce the spectra differences under time-varying conditions, and also detect FOD fault during operation, which are challenging to identify using conventional methods.

Order-frequency Holo-Hilbert spectral analysis for machinery fault diagnosis under time-varying operating conditions

Holo-Hilbert spectral analysis (HHSA) has been demonstrated to be an effective instantaneous feature demodulation tool for revealing the coupling relationship between the frequency-modulated (FM) carriers and amplitude-modulated (AM) characteristics within nonlinear and non-stationary mechanical vibration signals. However, it is unable to acquire the time varying AM characteristics from the vibration signals of the equipment operates under variable speed conditions. To decode such signals, inspired by HHSA, a novel angle-time double-layer decomposition structure termed order-frequency HHSA (OFHHSA) is established to demodulate the fault information from the time varying vibration signals in this paper. The corresponding spectrogram, namely, order-frequency Holo-Hilbert spectrum (OFHHS) is acquired for describing the interaction relationship between time and angle domains. Besides, the order AM-marginal spectrum is derived from the OFHHS via integrating the carrier variable to exhibit the fault characteristic-related orders. Moreover, the differences between OFHHSA and angle-time cyclo-stationary framework-based order-frequency spectral correlation (OFSC) are analyzed for time varying machinery fault diagnosis. Finally, from the analyses of simulated and tested data of mechanical equipment, the OFHHSA method has avoided the limitations of the two OFSC estimators on periodic assumption and the maximum cut-off order, and the proposed method obtained a more accurate rate in fault identification and more robust ability of anti-noise.

A whole service time SHM damage quantification model hierarchical evolution mechanism

Accurate damage quantification for an individual aircraft by using structural health monitoring (SHM) technology poses a persistent challenge in practical engineering. Existing damage quantification models typically rely on prior data acquired from ground design and tests. However, an individual aircraft is subject to diverse uncertainties throughout its whole service time, such as time-varying environmental and operational conditions, different flight missions, and different damage morphologies. Consequently, employing a prior trained model for damage quantification inevitably introduces errors, thereby limiting the engineering applicability of SHM. To improve the damage quantification accuracy for individual aircraft under the influence of uncertainty, a new whole lifetime data-based damage quantification model hierarchical evolution mechanism is proposed in this paper. Multi-source data from the design, service, and maintenance stages of individual aircraft are adopted to continuously evolve the probabilistic damage diagnosis model, enabling it to characterize the specific data distribution and track the damage propagation. The proposed method is validated through fatigue tests of a landing gear beam structure, which is an important aircraft load-carrying component, with guided wave monitoring based hidden Markov model quantifying its crack propagation. The validation result demonstrates the effectiveness of the proposed method in ensuring the long-term reliability of the model and achieving more accurate damage quantification for an individual aircraft.

Remaining Useful Life Prediction Method Based on Multisensor Fusion Under Time-Varying Operating Conditions

Remaining Useful Life Prediction Method Based on Multisensor Fusion Under Time-Varying Operating Conditions

An Adaptation-Aware Interactive Learning Approach for Multiple Operational Condition-Based Degradation Modeling.

Although degradation modeling has been widely applied to use multiple sensor signals to monitor the degradation process and predict the remaining useful lifetime (RUL) of operating machinery units, three challenging issues remain. One challenge is that units in engineering cases usually work under multiple operational conditions, causing the distribution of sensor signals to vary over conditions. It remains unexplored to characterize time-varying conditions as a distribution shift problem. The second challenge is that sensor signal fusion and degradation status modeling are separated into two independent steps in most of the existing methods, which ignores the intrinsic correlation between the two parts. The last challenge is how to find an accurate health index (HI) of units using previous knowledge of degradation. To tackle these issues, this article proposes an adaptation-aware interactive learning (AAIL) approach for degradation modeling. First, a condition-invariant HI is developed to handle time-varying operation conditions. Second, an interactive framework based on the fusion and degradation model is constructed, which naturally integrates a supervised learner and an unsupervised learner. To estimate the model parameters of AAIL, we propose an interactive training algorithm that shares learned degradation and fusion information during the model training process. A case study that uses the degradation data set of aircraft engines demonstrates that the proposed AAIL outperforms related benchmark methods.

Hybrid system response model for condition monitoring of bearings under time-varying operating conditions

Hybrid system response model for condition monitoring of bearings under time-varying operating conditions

A Hydraulic Axial Piston Pump Fault Diagnosis Based on Instantaneous Angular Speed under Non-Stationary Conditions

Due to the intense noise interference in hydraulic systems, it is extremely difficult to detect component faults through vibration signals. Diagnostic performance is also constrained by highly time-varying and non-stationary operating conditions. This study proposes to use instantaneous angular speed (IAS) signals that are both operational and state parameters as sources of information. Firstly, the instantaneous angular speed fluctuation (IASF) of a piston pump is analyzed theoretically, and it is concluded that its fluctuating components contain the health status information of the components. The IASF can then be obtained by subtracting the speed trend term from IAS signals obtained via a magneto-electric speed sensor. A synchro-extraction of the normal S transform (SNST) is proposed to process it via line-pass filtering. Finally, the filtered and reconstructed IASF signal is utilized to draw a two-dimensional polar coordinate map online. A non-stationary-condition test is carried out on the test platform to monitor the morphological characteristics of the valve plate under normal, slight, and severe wear conditions. The polar plot shows significant increases in speed fluctuations and oscillation times within a range from 180° to 270°. The relevant research results reflect that the IAS signal can provide a new method for monitoring the operating status of and conducting fault diagnoses for hydraulic equipment.

Construction of bearing health indicator under time-varying operating conditions based on Isolation Forest

While there have been extensive studies in the health assessment of the bearings using the vibration signal, most have focused on the constant operating conditions. The field, however, operates often under time-varying conditions as is the case of the automotive wheel bearing. In this study, a novel health indicator (HI) is proposed to address this problem based on the Isolation Forest algorithm, which was originally developed for anomaly detection. The method is advantageous in two aspects: the HI is not influenced by the type of operating conditions whether it is constant or time-varying. Only the data under normal condition are used to construct the HI without the need of run-to-failure data. The method is demonstrated by the three cases with different types of bearing and operating conditions ranging from constant to the highly variable conditions. As a result, monotonic trends are obtained for the HI in all cases, which may be useful for the prognostic monitoring. Furthermore, in comparison with the HI constructed by the run-to-failure data in the previous literature, it is found that the trends of HI agree reasonably well with each other, which supports the validity of the method.

On data-driven modeling and control in modern power grids stability: Survey and perspective

Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind turbines, energy storages, flexible loads, electric vehicles (EVs), etc. The grid control is confronted with low inertia, uncertainty and nonlinearity that challenge the operation security, efficacy and efficiency. The ongoing digitization of power grids provides opportunities to address the challenges with data-driven and control. This paper provides a comprehensive review of emerging data-driven dynamical modeling and control methods and their various applications in power grid. Future trends are also discussed based on advances in data-driven control.

On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM)

Since tool wear during machining process imposes a significant limitation on production quality as well as efficiency, on-line tool wear monitoring (TWM) is one of considerably crucial issues for intelligent manufacturing system. However, there is still a lack of universal on-line TWM techniques appropriate for the practical production process with variable machining parameters. Therefore, this study proposes a highly condition-adaptive method for tool wear state monitoring and remaining useful life (RUL) estimation under time-varying operation conditions. Multi-domain features are first extracted from vibration, acoustic emission and sound signals, and subsequently fused by the kernel principal component analysis (KPCA) to highlight discriminative information correlated to tool wear. A condition-adaptive hidden semi-Markov model (CAHSMM) is established to describe the evolution of tool wear, in which the accelerated failure time model (AFTM) embedded with Taylor tool life equation is utilized to determine the state transition probabilities that are strongly dependent on cutting parameters and duration time. Moreover, to extrapolate the Gaussian mixture models (GMMs) used to represent the observation probability beyond the range where they were trained, a Moment matching based on-line unsupervised transfer learning (OUTL) method is developed. After obtaining the state transition and observation probabilities, a modified forward algorithm is eventually implemented so as to assess tool wear online. Both the dataset from milling experiments of Ti6Al4V and PHM 2010 are employed for verifying the effectiveness of the proposed method. The results show that the presented method enables to reliably evaluate wear state and RUL of the cutter even under time-switching operation parameters, which demonstrates its promising potential for industrial applications.

A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions.

The diagnosis of bearing faults is an important guarantee for the healthy operation of mechanical equipment. Due to the time-varying working conditions of mechanical equipment, it is necessary to achieve bearing fault diagnosis under time-varying working conditions. However, the superposition of the two-dimensional working conditions of speed and acceleration brings great difficulties to diagnosis via data-driven models. The long short-term memory (LSTM) model based on the infinitesimal method is an effective method to solve this problem, but its performance still has certain limitations. On this basis, this article proposes a model for fault diagnosis under time-varying operating conditions that combines a residual network model (ResNet) and a gate recurrent unit (model) (GRU). Firstly, the samples were segmented, and feature extraction was performed using ResNet. We then used GRU to process the information. Finally, the classification results were output through the output network. This model could ignore the influence of acceleration and achieve high fault diagnosis accuracy under time-varying working conditions. In addition, we used t-SNE to reduce the dimensionality of the features and analyzed the role of each layer in the model. Experiments showed that this method had a better performance compared with existing bearing fault diagnosis methods.

A spectral self-focusing fault diagnosis method for automotive transmissions under gear-shifting conditions

Transmissions are core components of automobiles in adjusting the speed. The time-varying operation conditions, particularly the frequent gear shifting, present significant challenges for condition monitoring and fault locating of transmissions. At present, most existing studies primarily focus on the issues arising from time-varying speed, but they do not consider the challenges introduced by gear shifting. Gear shifting changes the meshing gear pairs within the transmission, causing the monitoring indicator amplitude fluctuates greatly. Consequently, the indicator fails to reflect the overall degradation trend of the transmission. To address this problem, this paper proposes a fault diagnosis method for automotive transmissions with the consideration of gear shifting. Our contributions are as follows: Firstly, we propose a spectral variation sparsity indicator (SVSI) based on the order spectrum at each gear position. Secondly, we fuse SVSI from different gear positions and create a comprehensive indicator called weighted health indicator (WHI). Finally, a diagnosis method based on SVSI and WHI is proposed for automotive transmissions under gear-shifting conditions. The effectiveness of the proposed method is validated using datasets from four automotive transmissions. The results demonstrate that our method is capable of detecting faults prior to inspection and accurately identifying faulty gears. Moreover, the performance of WHI is compared with several existing indicators. The results demonstrate that WHI exhibits stronger correlation, monotonicity, and robustness. This indicates that WHI is able to effectively mitigate the influence of gear shifting, thereby better reflecting the overall degradation trend of the transmission. Consequently, our proposed method significantly contributes to the field of condition monitoring and fault locating for automotive transmissions.

Power Allocation Strategy for Urban Rail HESS Based on Deep Reinforcement Learning Sequential Decision Optimization

A hybrid energy storage system (HESS) is adopted to tackle the traction network voltage fluctuation problem caused by high power and large energy demand during the starting and braking of urban rail trains. The system is composed of on-board ultracapacitors and ground lithium batteries, aiming to smooth out the power fluctuation to realize “peak-shaving and valley-filling”. Based on deep reinforcement learning (DRL) online sequence decision, a dynamic power allocation strategy is proposed to improve the energy-saving and voltage stabilization of DC traction networks as well as HESS life protection. Furthermore, to enhance the DRL’s efficiency under time-varying operating conditions, an annealing bias - priority experience replay twin delayed deep deterministic policy gradient algorithm (A-TD3) is proposed to train the replay buffer in DRL. The online learning and optimization strategy is implemented via the mechanism of “trial and error” and “feedback” of the agent. RT-LAB semi-physical real-time simulation systems are adopted to verify the effectiveness of the proposed strategy. Compared with the traditional filtering algorithms and DRL algorithms, the results show that the proposed method converges faster and is more energy saving and stable while effectively protecting the energy storage components.

Cooperative multi-agent deep reinforcement learning based decentralized framework for dynamic renewable hosting capacity assessment in distribution grids

Renewable hosting capacity (RHC) means the total renewable power that can be integrated into the power grid without violation of network constraints. In this paper, a cooperative multi-agent deep reinforcement learning (CMADRL) based decentralized method is proposed to assess the dynamic renewable hosting capacity (RHC) of distribution grids, aiming to duly make decisions for renewable energy interconnection requests and ensure consistent power grids reliability simultaneously. According to the time-varying load-generation operation conditions, the proposed CMADRL method can continuously derive multi-timescale operation strategies for volt-var control devices, e.g., static var compensators (SVCs) and on-load tap changer (OLTC), improving the RHC of distribution grids. With three independent agents (SVC, OLTC and renewable agents), the proposed CMADRL method follows the manner of centralized training and decentralized execution, which guarantees the algorithm convergence under time-varying load-generation uncertainties and meanwhile ensures the feasibility of online applications. The case studies are carried out on a modified IEEE 37-node distribution system to demonstrate the effectiveness of the proposed real-time RHC assessment method. Numerical results verify that the proposed CMADRL method has a better performance than conventional optimization methods on computation efficiency.

A hybrid prognostic method for rotating machinery under time-varying operating conditions by fusing direct and indirect degradation characteristics

Condition monitoring technologies can provide sensor data for predicting the remaining useful life (RUL) of rotating machinery. However, it is often difficult to obtain direct degradation characteristics (DCs), which directly reflect the health of a machine, in real-time. Instead, indirect DCs, which are collected under time-varying operating conditions, are often used. Traditional machine learning and model-based prognostic methods may not be effective when handling such data. This paper presents a hybrid Direct-Indirect Fusion (DIF) method that combines direct and indirect DCs to predict the RUL of rotating machinery under time-varying conditions. The framework can account for time-varying covariates, convert indirect DCs to direct DCs under moderate sample sizes with a loop-generative adversarial network (Loop-GAN), and describe gradual degradation and sudden shocks in direct DCs with Lévy processes. The proposed framework outperforms several benchmarks in predicting the degradation path and RUL of rotating machinery as demonstrated in both simulation examples and an industrial application.

AC Loss Modeling of a Full-Size ITER CS Module

Thorough analyses of electromagnetic transients in superconducting coils for fusion magnets are necessary to compute the losses arising in time-varying operating conditions and correctly design the cryogenic system. The challenge of these analyses, when using numerical methods, is related to the high number of degrees of freedom required to discretize large-scale magnets wound with cable in conduit conductors (CICCs), which include hundreds of composite strands. This study is focused on the central solenoid (CS) of the ITER magnet system, composed of six modules, each wound with several CICC for a total length of 6 km. The numerical code THELMA and an analytical model based on a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single time constant</i> —previously applied to analyze the tests of a 44-m-long single-layer solenoid, referred to as CS Insert (CSI)—are scaled up to calculate losses during factory acceptance tests (FAT) at the General Atomic (GA) in Poway (US). We demonstrate, by comparison with experimental data, the possibility of predicting the losses in the CS module by projecting the results obtained in the CS Insert subsize test, a single-layer solenoid tested at the National Institute for Quantum Science and Technology (Naka, Japan). The application of a numerical model discretizing the conductor with a few tens of subcables to a full-scale fusion magnet is very demanding. A novel methodology is proposed to abate the computational burden, based on reducing the number of turns involved in the calculation, still retaining a high level of accuracy in the loss assessment.

A physics-informed feature weighting method for bearing fault diagnostics

Intelligent bearing diagnostics has gained popularity over the last few years. However, most of the diagnostic methods are developed under the assumption that training and test data sets are collected under the same working conditions. This assumption is rare in practical scenarios because rotating machinery usually works under wide ranges of rotational speeds and loads. As bearings work under complex and time-varying operating conditions, the test data might come from a data distribution outside the training distribution. Purely data-driven diagnostic models often cannot provide reliable classifications for out-of-distribution test data. To tackle this challenge, this paper proposes a physics-informed feature weighting method for bearing diagnostics. First, a signal processing step is proposed that leverages physical knowledge of bearing faults to extract discriminative features that are robust to bearing speed variation. Then, a novel physics-informed feature weighting layer is developed to assign higher weights for features located closer to bearing fault characteristic frequencies. The feature weighting layer enhances the model’s sensitivity towards the fault-related features among the speed invariant features. Through experiments on three bearing datasets, the effectiveness of the proposed method is validated and shown to have promise for bearing fault diagnostics under different operating conditions. This study also details the deployment of a physics-informed convolutional neural network model on an Industrial Internet of Things (IIoT) device, where edge computing gives users a real-time evaluation of bearing health.