Performance Degradation Assessment Method Research Articles

Assessment of Hull and Propeller Performance Degradation Based on TSO-GA-LSTM

Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

The performance degradation assessment (PDA) of key components such as gears and rolling bearings is the core technology of prognostics and health management for rotating machinery. Most existing PDA methods may have two deficiencies: (1) the assessment indicator constructed does not consider capturing fault characteristics directly; (2) feature selection is generally based on the measured data of different fault levels, which is difficult to obtain in actual processes; moreover, the selection results lack universality and are difficult to extend to other equipment. To address these issues, this paper proposes a novel PDA method based on fault information and dynamic simulation. First, anomaly detection is performed using four well-known indicators in combination with Mahalanobis distance. Secondly, fault identification is performed using envelope spectrum analysis on anomaly signals to determine the fault type, e.g. gear fault or outer race fault. Thirdly, based on the fault type information, the candidate feature set including fault-domain indicators is selected based on the established dynamic simulation signals to obtain a preliminary assessment vector for the first stage. The stability of the fault domain indicators which capture fault characteristics directly is tested through actual measured normal data. It is used as the second stage of selecting to obtain the assessment vector. Finally, the PDA indicator is calculated based on the assessment vector and Mahalanobis distance. Four experiment case studies demonstrate the proposed PDA method can effectively isolate faults with different defect sizes as well as track the whole performance degradation. The above analysis indicates that the proposed PDA method is expected to be used for the actual rotating machinery.

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

Bearing operation states will directly determine the performance of the equipment; thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset.

Mechanical Equipment Performance Degradation Assessment Method Based on Improved Degradation Feature Extraction and Sample Completeness

Performance degradation assessment (PDA), as an important part of health management, is playing a crucial role in evaluating the degenerate state of mechanical equipment. To evaluate the performance degradation state more effectively by using monitoring data of mechanical equipment, a PDA method based on improved degradation feature extraction technology and considering sample completeness is proposed in this paper. Firstly, multiple degradation features, including statistical features and intrinsic energy features, are extracted to construct a high-dimensional feature set calculated by time-domain analysis and the EMD method. Then, a sensitive feature set, which has significant robustness, correlation, and monotonicity in the degenerate process, is reduced and processed by the improved PCA method to obtain the final health index. Secondly, the degradation assessment model is established by considering the completeness of the samples. As for the complete sample dataset, which has both normal and failure state data, a logistic regression model (LRM) is built to assess the performance degradation status. In addition, an SVDD model is established for the incomplete sample dataset, which only has normal state data. Finally, the reliability of the proposed method is verified by the public XJTU-SY dataset, and the comparative experiment further verifies the superiority of this method.

Hob performance degradation assessment method based on cyclic statistical energy

Hob performance degradation assessment method based on cyclic statistical energy

Rolling bearing performance degradation assessment based on deep belief network and improved support vector data description

Rolling bearing performance degradation assessment (PDA) based on vibration signal is critical to intelligent maintenance. However, there are two problems in existing methods: 1) degradation signals are needed as building model, 2) feature extraction is dependent on experience and operation parameters. Aiming at these challenges, a PDA method based on deep belief network (DBN) and improved support vector data description (SVDD) is proposed in this paper. The normalized amplitude spectrum under normal state is used as the training sample of DBN without the classified output layer, which is used as the automatic feature extraction model. Then, the SVDD which requires appropriate penalty parameter C and kernel parameter σ is employed to fuse these features into performance indicator (PI), and the sparrow search algorithm (SSA) is introduced into the parameters optimization process of SVDD. The results applied in two experiment datasets show that the proposed method can detect the occurrence of incipient degradation and reflect the whole bearing performance degradation. And the advantages over other methods have been shown. In addition, the anti-noise capability, compared with RMS, is discussed, and the results show that the proposed method is better than RMS to some extent, which is worth being researched further in principle.

A valid and efficient mechanical performance degradation assessment method for cushion member material EPDM based on DQ NMR inspection and static compression test

The aim of this study is to establish a valid and efficient inspection method for assessing macroscopic mechanical property degradation of ethylene-propylene-diene-monomer (EPDM) by examining crosslinked density variation of microstructure. Inspection methods of mechanical behavior and crosslinked density are based on static compression and double quantum nuclear magnetic resonance ( DQ NMR), respectively. After introducing the hyperelastic constitutive model proposed by Yuhai Xiang and designing different conditions of heat-aging and fatigue load, the significant mechanical parameter variation is observed for the hyperelastic constitutive model. The significant impacted factors include heat-aging time and fatigue load except for heat-aging compression strain. The phenomenon is consistent with DQ NMR test results. We further analyze relationship between macro and micro properties of EPDM. After importing fitting assumption and limitation of DQ NMR data to control fitting error, the fractions of dangling and sol chains and residual dipolar couplings constants in DQ NMR parameters show moderate negative () and high positive () correlation with of constitutive model parameters, respectively. Thus the validity and precision of inspection method is proven to estimate behavior variation of EPDM and hopefully the method can be extended to analyze other rubber-like materials.

Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter

The evolution of performance degradation has become a major obstacle to the long-life operation of the Solid Oxide Fuel Cell (SOFC) system. The feasibility of employing degradation resistance to assess the State of Health (SOH) is proposed and verified. In addition, a real-time Unscented Kalman Filter (UKF) based SOH estimation method is further proposed to eliminate the disturbance of calculating the SOH directly utilizing measurement and electric balance model. The results of real-time SOH estimation with an UKF under constant and varying load conditions demonstrate the feasibility and effectiveness of the SOFC performance degradation assessment method.

A performance degradation assessment method using one-dimensional sparse representation self-learning dictionary

Rotating machinery is a widely-used component of factories and enterprises. Unquestionably, rolling bearing is the backbone of rotating machinery and its performance degradation assessment (PDA) is a vital part of condition-based intelligent health management. Therefore, a PDA method based on one-dimensional sparse representation self-learning dictionary is presented to assess the degradation trend of rolling-element bearings. We proposed a novel feature extraction method named one-dimensional K-singular value decomposition to extract the sensitive feature from run-to-failure data. Then, an improved support vector data description is adopted to establish the PDA model, and a novel degradation indicator is obtained. Subsequently, the proposed method adaptively tracks the development of deterioration and identifies the occurrence of an incipient fault. Experimental results on the simulation signal and XJTU-SY bearing datasets verify the effectiveness and outperform the conventional methods.

Performance degradation assessment methodology of harmonic reducer by using low-frequency time series data and genetic programming

The harmonic reducer, as an important transmission component in the industrial robot, determines the positioning accuracy, carrying capacity, and service life of the industrial robot. However, the performance and reliability of harmonic reducer will gradually decline or even fail under long-time and high-intensity operations. The sudden stop and failure of the industrial robot will directly affect production efficiency. In this paper, a data-driven method for performance degradation assessment (PDA) of the harmonic reducer is studied. An accelerated degradation test rig was set up to collect the run-to-failure datasets of harmonic reducer, including process data, such as torque and speed, and non-process data, such as vibration signals, which were captured under a low sampling frequency. Firstly, a sliding window-based kurtosis and root mean square (RMS) are applied to non-process data, which can detect the incipient failure time (IFT) of harmonic reducer. Subsequently, a data-level fusion of process data is conducted based on genetic programming to construct a monotonic health index for degradation assessment and trend analysis. The experimental study shows that the proposed method is effective to realize the PDA of harmonic reducer.

A generalized health indicator for performance degradation assessment of rolling element bearings based on graph spectrum reconstruction and spectrum characterization

The performance degradation assessment (PDA) of rolling element bearings is a necessary link to ensure the reliability of high-end equipment. However, traditional health indicators (HIs) are not sensitive to early defects, and there are often large local fluctuations in the later stage of degradation. Hence, this paper propose a novel PDA method to obtain HIs with early warning capability and monotone trend. Firstly, an improved graph spectrum reconstruction method is proposed to enhance the characteristics of signals. The random phase space reconstruction strategy is introduced to solve the problem of large-scale graph Laplacian matrix decomposition. Then, the spectrums of the enhanced signals are characterized, namely the high-dimensional degradation features in frequency domain are extracted and smoothed by Kalman filter. Finally, the Laplacian Eigenmaps is used to extract the intrinsic degeneration manifolds from the high-dimensional degradation features as the established HIs. Life cycle degradation data and quantitative failure data are analyzed to verify the effectiveness of the proposed method. Compared with other state-of-art methods, the results show that the HIs established by the proposed method reflect the degradation earlier and have obvious degradation trend. It effectively realizes the mapping between degradation and HI.

Bearing performance degradation assessment based on topological representation and hidden Markov model

Bearing performance degradation assessment is essential to avoid abrupt machinery breakdown. However, background noise, outliers, and other interferences in the monitoring data may restrict the accuracy and stability of bearing performance degradation assessment in practical applications. In this study, a bearing performance degradation assessment method based on the topological representation and hidden Markov model is proposed. To construct a robust and representative feature space, the topological representations, specifically, topological meshes of the original features are obtained by self-organizing map, which can represent the general structure of the original feature space and eliminate outliers and other interferences. Then, the weight vectors of topological meshes are used as degradation features. Finally, the hidden Markov model is adopted as the assessment model to evaluate the bearing performance degradation tendency and detect the initial degradation effectively. To validate the effectiveness and superiority of the proposed method, two experimental datasets are analyzed. Compared with peer methods, the performance indicator curve of the proposed method presents a more smooth and accurate degradation tendency than comparative methods. Moreover, initial degradation can be identified accurately.

Research on the performance degradation assessment method of a mine hoist braking system based on variable step-size fruit fly optimization algorithm-complex Gaussian wavelet support vector data description.

According to the characteristics of the constant deceleration braking system of hoist, a variable step-size fruit fly optimization algorithm-complex Gaussian wavelet support vector data description method is proposed in this article to evaluate the performance of the brake system. This method takes the pressure-time curve of safety braking as the characteristic data and extracts the candidate characteristic parameters from it. After the comprehensive evaluation of the characteristic parameters based on correlation, monotonicity, and predictability, the characteristic parameters are selected to evaluate the overall performance of the brake system. The proposed variable step-size fruit fly optimization algorithm-complex Gaussian wavelet support vector data description model avoids the situation where the classical Drosophila optimization algorithm is easy to fall into the local optimum. The reliability of the proposed method is verified by the simulation data, and the practicability of the proposed method is verified by the test-bed data. The performance evaluation model of variable step-size fruit fly optimization algorithm-complex Gaussian wavelet support vector data description is constructed, which realizes the quantitative evaluation of the brake system health state and provides technical support for the condition-based maintenance and intelligent maintenance of the hoist brake system.

A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox

Gearbox is one of critical transmission components in wind turbine (WT) having a high downtime rate among all subcomponents. Fault prognostics and health management (PHM) of WT gearbox is crucial to their high reliability operation. However, presence of background noise in WT signals restricts the applicability of existing PHM approaches in feature extraction. To solve this problem, a novel performance degradation assessment method based on deep belief network (DBN) and self-organizing map (SOM) is proposed to de-noise and merge multi-sensor vibration signals. Minimum quantization error (MQE) is defined as health indicator to detect incipient fault of WT gearbox. After health indicator construction, an improved particle filtering (PF) optimized by fruit fly optimization algorithm (FOA) is employed to predict the remaining use life (RUL) of WT gearbox. To take advantage of dynamic and random operation process of WT gearbox, Wiener-process-based degradation model is developed to improving RUL prediction efficiency. The effectiveness is validated by using simulated as well as experimental vibration signals obtained through a WT gearbox highly accelerated life test. The results illustrate that proposed method can evaluate performance degradation process and predict RUL of WT gearbox effectively.

Performance degradation assessment of slewing bearings based on deep auto-encoder and optimized particle filtering

Due to the low speed and heavy load conditions of slewing bearings, extracting of effective features for fault diagnosis and prediction is difficult but crucial. Moreover, challenges such as large data volumes, unlabeled and multi-source bring more difficulties for advanced prognosis and health management methods. To solve these problems, a novel method for performance degradation assessment of bearings based on raw signals is proposed. In this methodology, a combination of deep auto-encoder (DAE) algorithm and particle filter algorithm is utilized for feature extraction and remaining useful life (RUL) prediction. First, the raw vibration signal is employed to train parameters of a restricted Boltzmann machine to build the DAE model. Through encoding and decoding multi-source data, root mean square error of reconstruction error between the raw signal and reconstructed signal is employed to detect incipient faults of slewing bearings. Then, degradation trend model is established by particle filtering to predict RUL of bearings. The effectiveness of proposed method is validated using simulated and experimental vibration signals. Results illustrate that proposed method can evaluate the performance degradation process and RUL of slewing bearings.

Assessing the Performance Degradation of Lithium-Ion Batteries Using an Approach Based on Fusion of Multiple Feature Parameters

A method based on fusion of multiple features is proposed to assess and accurately describe the performance degradation of lithium-ion batteries in this paper. First, the discharge voltage signal of lithium-ion batteries under real-time monitoring is analyzed from the perspective of time domain and complexity to obtain the values of multiple features. Then, the multi-feature parameters undergo a spectral regression process to reduce the number of dimensions and to eliminate redundancy, and on the basis of this regression, a Gaussian mixture model is established to model the health state of batteries. Thus, the degree of lithium-ion battery performance degradation can be quantitatively assessed using the Bayesian inference-based distance metric. A case calculation experiment is carried out to verify the effectiveness of the method proposed in this paper. The experimental results demonstrate that, compared with other assessment methods, the performance degradation assessment method proposed in this paper can be used to monitor the degradation process of lithium-ion batteries more effectively and to improve the accuracy of condition monitoring of batteries, thereby providing powerful support for making maintenance decisions.

Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion

Gearbox is a critical transmission component in the drivetrain of wind turbine having a dominant failure rate and a highest downtime loss among all wind turbine subsystems. Ensemble empirical mode decomposition and principal component analysis have been extensively investigated for signal decomposition and fault feature extraction from the signals of a wind turbine gearbox. However, presence of background noise in wind turbine signals restricts the applicability of these algorithms in real scenarios. To solve this problem, a novel performance degradation assessment method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA) was proposed to de-noise and fuse vibration signals. A comparison is conducted between CEEMDAN-KPCA and other five cross combinations. After feature extraction, extreme learning machine (ELM) optimized by fruit fly of algorithm (FOA) is employed to predict the remaining use life (RUL) of wind turbine gearbox. To avoid the tedious hand-crafting hidden nodes, a dynamic adjustment strategy is presented for the improvement of search efficiency. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. The results illustrate that the CEEMDAN-KPCA gets better result in signal de-noising. Compared with different swarm intelligence algorithms, the RUL prediction rates of wind turbine gearbox are improved by using the FOA-ELM prediction model with multiple parameters.

Rolling Bearing Performance Degradation Assessment Based on Convolutional Sparse Combination Learning

A novel bearing performance degradation assessment method based on convolutional sparse coding and combination learning is proposed in this paper, which can avoid the impact of the traditional features on the assessment results under complex operation conditions. The vibration signal can be decomposed into the convolution of the kernel sets and their corresponding sparse solutions using convolutional sparse coding. The learned kernel sets based on the training signal samples are the comprehensive embodiment of the information related to the operational complexity and the bearing healthy state, and the corresponding sparse solutions indicate the energy of the kernel activation. Asimulation experiment of bearing signal proves that the activation energy of the kernels, which are more related to bearing healthy, will rise with the increase of the bearing degradation degree; meanwhile, the error of reconstructed signal based on the learned kernel sets and original signal will decrease since the description effect of convolutional sparse coding to the signal will be enhanced with the periodic strengthen of signal caused by the fault. Thus, an index based on the kernel sparse norms and the errors between the original and reconstructed signals has been proposed to evaluate the degradation degree of the bearing in this paper. On the other hand, combination learning will be fused into the convolutional sparse coding to improve the real-time performance of the index calculation and obtain the best assessment result in the testing part by dividing the kernel dictionary set into multiple sub-dictionaries. A bearing run-to-fail experiment is analyzed to verify the validity of the proposed method. The testing results show that the proposed assessment index can clearly detect the initial fault, serve fault and failure of the bearing in the whole life, and the proposed method is more self-adaptive and sensitive to the fault degree. In addition, it is verified that the time consumption of the combination dictionaries is less than that of a single dictionary set.

Bearing performance degradation assessment using long short-term memory recurrent network

Bearing is commonly used in rotating machinery, and it is significant to monitor bearing running states to ensure machine safety. Performance degradation assessment is an important work in bearing condition-based maintenance (CBM) and predictive maintenance. In this paper, a data-driven bearing performance degradation assessment method based on long short-term memory (LSTM) recurrent neural network (RNN) is proposed to comprehensively utilize the fault propagation information. Firstly, universal degradation simulation model based on vibration response mechanism is constructed for feature verification. A new proposed indicator "waveform entropy (WFE)" is developed and validated by this simulation model. Then waveform entropy and other conventional indicators are input into the LSTM RNN to identify the bearing running state, while particle swarm optimization method is applied to optimize the network structure parameters simultaneously. Experimental results demonstrate that LSTM RNN can effectively identify the bearing degradation states and accurately predict the remaining useful life.