Multivariate State Estimation Research Articles

Health status assessment of unmanned aerial vehicle (UAV) attitude control system based on an improved multivariate state estimation method

Health status assessment of unmanned aerial vehicle (UAV) attitude control system based on an improved multivariate state estimation method

Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology

Abstract. In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice concentration, thickness and velocity, but also its level of damage, internal stress and cohesion. Specifically, we estimate the air drag coefficient and the so-called damage parameter of the MEB model. Mimicking the realistic observation network with different combinations of observations, we demonstrate that various issues can potentially arise in a complex sea-ice model, especially in instances for which the external forcing dominates the model forecast error growth. Even though further investigation will be needed using an operational (a coupled dynamics–thermodynamics) sea-ice model, we show that, with the current observation network, it is possible to improve both the observed and the unobserved model state forecast and parameter accuracy.

EMA Health Indicator Extraction Based on Improved Multivariate State Estimation Technique With a Composite Operator

EMA Health Indicator Extraction Based on Improved Multivariate State Estimation Technique With a Composite Operator

Multi-scale adaptive multivariate state estimation fault detection enhancement for time-varying industrial system based on multi-output Gaussian process autoregression

Most chemical processes are time-varying and nonlinear due to load changes, product demand transitions or other causes, causing unsatisfactory performance of conventional non-parametric multivariate statistical fault detection method. In this work, a probabilistic multivariate state estimation fault detection method based on multi-output Gaussian process autoregression is first developed to overcome the linear limitation and noise information ignorance of multivariate state estimation technology-based fault detection methods. In multi-output Gaussian process regression framework, the Leave-One-Sample-Out strategy is developed to construct the dataset by leaving each historical sample from the memory matrix out as one training output in turn while regarding the corresponding remaining historical samples as one training input. Then, we design the multi-output Gaussian process autoregression model to estimate the current state of the system. Utilizing the estimation covariance information, a probabilistic index based on multivariate Kullback-Leibler divergence is developed for fault detection. Moreover, a multi-scale adaptive fault detection enhancement based on discrete multi-objective Archimedes optimization and just-in-time learning is further proposed to handle the time-varying problem. In this scheme, the memory matrix is batch-adaptively updated at monthly intervals based on discrete multi-objective Archimedes optimization to handle the slow change of system characteristics caused by equipment degradation. On the other hand, to cope with the rapid change of system characteristics caused by operation transition, the just-in-time learning is employed to select samples from the memory matrix that best match to the current system state for sample-adaptive updating. Finally, two industrial cases are used to verify the performance of the proposed method.

A novel fault early warning method for mechanical equipment based on improved MSET and CCPR

Prompt fault early warning is a significant approach to improve the reliability of mechanical equipment. An abnormal state recognition method based on improved multivariate state estimation technology (MSET) and control chart pattern recognition (CCPR) is proposed. First, the health data band (HDB) of equipment is established through a k-nearest neighbor algorithm. Meanwhile, a dynamic memory matrix construction approach based on a clustering algorithm is presented to improve the calculation efficiency of MSET. The CCPR is integrated to reduce limitations such as artificial interference in the traditional threshold method of fault early warning. The proposed method is applied into two cases of rolling bearing and control moment gyro so as to compare its performance with other MSET modeling methods. According to the results, the proposed method can detect equipment abnormalities hours to weeks in advance, and significantly reduce the false alarm rate and calculation time.

Vibration Resonance Spectrometry (VRS) for the Advanced Streaming Detection of Rotor Unbalance

Determination of the diagnosis thresholds is crucial for the fault diagnosis of industry assets. Rotor machines under different working conditions are especially challenging because of the dynamic torque and speed. In this paper, an advanced machine learning based signal-processing innovation termed the multivariate state estimation technique is proposed to improve the accuracy of the diagnosis thresholds. A novel preprocessing technique called vibration resonance spectrometry is also applied to achieve a low computation cost capability for real-time condition monitoring. The monitoring system that utilizes the above methods is then applied for prognostics of a fan model as an example. Different levels of radial unbalance were added on the fan and tested, and then compared with the health state. The results show that the proposed methodology can detect the unbalance with a good accuracy and low computation cost. The proposed methodology can be applied for complex engineering assets for better predictive monitoring that could be processed with on-premise edge devices, or eventually a cloud platform due to its capacity for loss-less dimension reduction.

Harmonic Reducer Performance Prediction Algorithm Based on Multivariate State Estimation and LargeVis Dimensionality Reduction

As an important transmission component of industrial robots, the harmonic reducer determines the positioning accuracy, bearing capacity and service life of the robot end-effector. Predicting the performance can grasp the working status in advance and avoid major losses caused by uncertain factors such as component damage. The current paper focuses on a harmonic reducer performance prediction algorithm based on Multivariate State Estimation Technique (MSET) and LargeVis dimensionality reduction. Firstly, an accelerated life test platform is designed to collect multi-dimensional parameters that can characterize the operating state of the harmonic reducer throughout the life cycle. Afterwards, as far as the MSET method is concerned, the fault warning threshold is set according to the residual between the constructed memory matrix of the health state data and the actual observed value. Finally, utilizing LargeVis to reduce the dimensionality of multi-dimensional features, combining with Mahalanobis distance to construct a health index degradation model, and then selecting Long Short-Term Memory (LSTM) network to predict the downward trend of the harmonic reducer. The analysis of the accelerated life test data of the harmonic reducer demonstrates that the proposed method can send out the fault warning signal 18 minutes in advance in the sample with a life of 5.7 hours, and has a strong ability to predict the degradation trend of the harmonic reducer.

An Adaptive Condition Monitoring Method of Wind Turbines Based on Multivariate State Estimation Technique and Continual Learning

With the development of wind energy, the condition monitoring (CM) methods of wind turbines (WTs) based on supervisory control and data acquisition (SCADA) data have attracted much attention to detect potential faults. With the impact of complicated internal and external factors, the operation conditions of WTs are time-varying. Thus it is necessary to adaptively update CM models in long-term operation. An adaptive WT CM method based on multivariate state estimation technique (MSET) and continual learning (CL) is proposed, which is concise and suitable for practical application. MSET is used to build the non-parametric and high-accuracy normal behavior model. In the proposed CL strategy, new normal data will be temporarily stored in the data buffer to realize the adaptive update of the MSET model. And rules for missing and abnormal data are designed to stabilize update frequency and improve fault detection ability respectively. The proposed method is validated using a real-world SCADA data set with gearbox faults. The results show that the proposed method has higher estimation accuracy and lower false alarm rate than other methods, and the proposed CL strategy has popularization potential. Related hyperparameters are discussed, and when using less training data, the proposed method still has better performance.

МЕТОДЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ ОБОРУДОВАНИЯ ПРОКАТНОГО ПРОИЗВОДСТВА. ДИАГНОСТИКА ПОДШИПНИКОВ ИЗГИБО-РАСТЯЖНОЙ МАШИНЫ

This paper discusses the problem of diagnosing the condition of bearings in a tension leveler of a sheet mill. Due to aggressive conditions and location features, traditional vibration diagnostic methods are not applicable. The paper proposes an innovative solution based on a multizone temperature sensor. To build a model of the normal behavior of the system, a multivariate state estimation model and a neural network of the autoencoder structure were used. A comparison of the models’ performance is provided.

A data-driven degradation prognostics approach for rolling element bearings

Degradation prognostic plays a crucial role in increasing the efficiency of health management for rolling element bearings (REBs). In this paper, a novel four-step data-driven degradation prognostics approach is proposed for REBs. In the first step, a series of degradation features are extracted by analyzing the vibration signals of REBs in time domain, frequency domain and time-frequency domain. In the second step, three indicators are utilized to select the sensitive features. In the third step, different health state labels are automatically assigned for health state estimation, where the influence of uncertain initial condition is eliminated. In the last step, a multivariate health state estimation model and a multivariate multistep degradation trend prediction model are combined to estimate the residence time in different health status and remaining useful life (RUL) of REBs. Verification results using the XJTU-SY datasets validate the effectiveness of the proposed method and show a more accurate prognostics results compared with the existing major approaches.

Risk Warning for Aircraft Bleed Air System with Multivariate State Estimation Technique

The engine bleed air system (BAS) is one of the important systems for civil aircraft, and its operating state directly affects the operational safety of the aircraft. The effective risk warning of the BAS is critical to improving aircraft safety and operators’ profits; thus, a multivariate state estimation technique with the dynamic process memory matrix is proposed to warn the failure risk of the bleed air system. First, to obtain the optimal estimation value of the observation vector, the memory matrix is formed by searching for the first vectors that are similar to each input observation vector from the healthy data pool that can cover the common working space. Then, the similarity function is defined to quantitatively measure the deviation between the observed vector and the estimated vector, and the amount of risk information contained in each variable is quantified by the analytic hierarchy process. Finally, the dynamic threshold different from the traditional engineering experience threshold is designed, based on the idea of interval estimation. The developed approach is validated on an Airbus A320-series aircraft with quick access recorder data for one year. The results show that the proposed strategy can provide an effective risk warning for the abnormal state of the BAS before a failure occurs.

Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique

With the development of wind turbine (WT) operation and maintenance technologies, the condition monitoring (CM) method based on the data of supervisory control and data acquisition (SCADA) systems has become one of the most popular and cheapest ways to detect the anomalies. As the operation condition of WT will change over time, it is necessary to real-time update the CM model with little manual maintenance to ensure its long-term performance. Therefore, a SCADA data-driven WTCM method based on incremental learning and multivariate state estimation technique (MSET) is proposed. Firstly, for the memory matrix (MM) of MSET, a similarity-based sample selection method is proposed, which is simpler and more effective than the previous method. Secondly, based on the proposed sample selection method, an incremental learning strategy for MSET is proposed, which can add the normal new data in MM and remove the redundant data in real-time. Aiming at the slow computation speed caused by the large MM, a dynamic down-sampling method is proposed, which makes only half of the data participate in the real-time computation and can reduce the computation time by at least 80%. About three years SCADA data of two different types of WTs are used to verify the proposed method. The experimental results show that the incremental MSET can maintain higher estimation accuracy and lower false alarm rate in long-term operation, and can detect the potential gearbox faults hours to weeks in advance.

Multivariate State Estimation Technique Combined with Modified Information Entropy Weight Method for Steam Turbine Energy Efficiency Monitoring Study

An energy efficiency monitoring method of the steam turbine system is studied in this paper. Multivariate state estimation technique (MSET) is utilized to compare the actual monitoring parameters and the healthy data of the equipment in normal working condition with a multi parameter estimation model. Due to the limitation of a single heat rate index in evaluating energy efficiency variation, the energy efficiency deviation degree combined with improved information entropy weight is proposed to judge the steam turbine’s operation condition levels. The index value in the modified weight method has been searched for more steady weight values calculated by information entropy values with small variation. Taking a 600 MW unit as an example, the energy efficiency levels of the unit under a 550 MW normal working condition are clustered into four groups, testifying the MSET model correctness and calculating the deviation degree value. Then, the energy efficiency status monitoring model is utilized to record residuals of actual data and estimated data during abnormal energy efficiency period. The residuals over deviation degree are then marked and judged as related with the abnormal data. The results show that the MSET model can timely and accurately judge the change of unit operation state, and the deviation degree calculated by the modified information entropy weight method can provide earlier warnings for the abnormal energy efficiency working conditions.

A robust strategy for sensor fault detection in nuclear power plants based on principal component analysis

The principal component analysis (PCA) method has been widely used in sensor fault detection. However, outliers of training data may affect the projection directions of both principal component (PC) and residual space, thereby reducing the fault detection rate (FDR). The high sensitivity of PCA to random noise in the test data can also lead to an increase in the false alarm rate (FAR). To improve the performance of the PCA, this paper proposes a robust PCA approach for sensor fault detection in nuclear power plants (NPPs). A statistical method based on Euclidean distance is used to clean outliers in the training data pre-processing phase. Subsequently in the fault detection phase, the moving average (MA) filtering method is adopted to process Q-statistic to reduce false alarms caused by random noise in the test data. Simulation and plant signals are used to verify the effectiveness of the proposed method. Finally, comparisons with the conventional PCA, auto-associative kernel regression (AAKR) and multivariate state estimation technique (MSET) highlight the advantages of the proposed method.

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

Wind turbine condition monitoring based on a novel multivariate state estimation technique

With the development of the wind energy industry, condition monitoring (CM) has become one of the important ways to improve the reliability of wind turbines (WTs). A data driven WTCM method based on a novel multivariate state estimation technique (MSET) is proposed, which can realize the fault early warning of WT components. In order to reduce the information redundancy of operational parameters, the maximum conditional mutual information (CMI) feature selection algorithm is applied to the training data of MSET. To improve the performance and flexibility of MSET, a dynamic memory matrix (MM) construction method based on k-nearest neighbor algorithm is proposed, which can provide a dynamic MM that varies in real-time with the current operational condition. A fast calculation method of the dynamic MM is also proposed, which can reduce the computation time by 15–26%. Two residual-based CM methods are proposed to realize fault early warning. The real-time method is based on the real-time residuals and if several consecutive residuals exceed the threshold, the fault alerts will be issued. The long-term method divides the historical residuals into day-level and analyzes them based on control charts. The proposed method is applied to two real cases of the gearbox and generator bearing overheating faults. The experimental results show that the maximum CMI algorithm and the dynamic MM significantly improve the estimation error of MSET. Compared with the recorded fault information, the proposed method realizes the fault early warning about 2–20 days in advance.

A Multivariate State Estimation Technique and Multilayer Forward Neural Network Framework on Steam Turbine Valve Flow Curve Simulation

The Multivariate State Estimation Technique (MSET) is firstly implemented as the fault detection technique for monitoring parameters of the steam turbine valve. Second, healthy operation states are remained to form a preliminary dataset for the valve curve simulation. Then, the Forward neural network (FNN) based piecewise linear fitting method is used to simulate the steam turbine valve flow curve. With a MSET-FNN framework proposed, the simulation accuracy will be enhanced by removing the unhealthy operational states. In addition, the simulation process will gain a speed up with the utilization of GPU.

Mathematical methods for visualization and anomaly detection in telemetry datasets.

Recent developments in both biological data acquisition and analysis provide new opportunities for data-driven modelling of the health state of an organism. In this paper, we explore the evolution of temperature patterns generated by telemetry data collected from healthy and infected mice. We investigate several techniques to visualize and identify anomalies in temperature time series as temperature relates to the onset of infectious disease. Visualization tools such as Laplacian Eigenmaps and Multidimensional Scaling allow one to gain an understanding of a dataset as a whole. Anomaly detection tools for nonlinear time series modelling, such as Radial Basis Functions and Multivariate State Estimation Technique, allow one to build models representing a healthy state in individuals. We illustrate these methods on an experimental dataset of 306 Collaborative Cross mice challenged with Salmonella typhimurium and show how interruption in circadian patterns and severity of infection can be revealed directly from these time series within 3 days of the infection event.

Auto-encoder based fault early warning model for primary fan of power plant

Primary fan system plays an important role in the operation of a power plant. However, due to the complicated working conditions of the primary fan and the strong coupling characteristics of multi-state variables, it is necessary to carry out feature engineering before using multivariate state estimation technique (MSET). In addition, no-linear operator should be designed to make sure matrix being invertible. This paper proposes an Auto-encoder based model to automatically construct a normal state memory matrix through unsupervised learning of neural networks. It reduces human intervention as well as the difficulty in giving a suitable non-linear operator design. This model is applied to the early warning of primary fan failure in a power plant in eastern Shandong Province.

Model for Early Detection of Emergency Conditions in Power Plant Equipment Based on Machine Learning Methods

The article discusses a method for early detection and prediction of abnormality in operation of power-unit process equipment taking as an example the PTN 1100-350-17-4 turbine driven feedwater pump of a 300 MW power unit. The importance of the problem of predicting possible process equipment malfunctions at an early state of their occurrence is determined, and the specific features of solving it in the power industry are explained. The range of process equipment defects that can be efficiently detected using the predictive analytics methods is outlined. The fundamental assertion stating that the scope of analog and discrete measurements available in the process control system’s set of computerized automation tools is sufficient for applying the predictive analytics methods is emphasized. Modern predictive analytics methods are briefly reviewed, and the specific features of model training algorithms are mentioned. Separate attention is paid to the problems of preparing initial data for training the model. The mathematical problem of modeling an abnormality indicator taking the values from 0 (normal operation) to 1 (abnormal operation) is formulated. In turn, this problem is formulated as the binary classification problem of attribute vectors characterizing the equipment state at the given moment of time. An original approach is suggested, which combines the multivariate state estimation technique (MSET), in which the degree of abnormality in a technical state is determined from the extent to which the Hotelling criterion exceeds a threshold level (which is automatically calculated in the algorithm), and machine learning methods, the use of which makes it possible to overcome a number of difficulties inherent in the MSET. For solving the problem of determining the composition of the most informative attributes from the values of which early development of an emergency can be detected, it is proposed to use an ensemble of regression models. A method for selecting the modeled variable and the set of regressors is substantiated. An abnormality indicator calculation method based on composing an ensemble of linear regression models is proposed, and the advantage of using an ensemble over a single classifier is shown. A method for producing an alarm in response to detected abnormality in the operation of power unit process equipment is suggested. It is shown that it became possible by using the proposed model to detect the onset of the emergency development process, whereas individual indicators failed to reveal pump operation singularities in the preemergency interval of time.