A generalized degradation tendency tracking strategy for gearbox remaining useful life prediction

Chen, Xieyi; Wang, Yi; Sun, Hao; Ruan, Hulin; Qin, Yi; Tang, Baoping

doi:10.1016/j.measurement.2022.112313

Cited by 10 publications

(5 citation statements)

References 30 publications

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“…As essential transmission components in rotating machinery, rolling bearings and gears are susceptible to various faults under harsh conditions [1]. The faults of such components can easily trigger abnormal equipment shutdowns and even catastrophic accidents [2][3][4][5][6]. Therefore, it is necessary to * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…Feature selection is the process of selecting specific feature indicators from a set that satisfies the degradation characteristics. Common characteristics used for feature selection include monotonicity, trend, and correlation [4,7,[33][34][35]. During the actual feature selection process, the selected feature subset may ideally satisfy the aforementioned characteristics simultaneously [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Qin

et al. 2024

Meas. Sci. Technol.

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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 and 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Qin

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…However, the time-varying braking torque with long-term operation has not been accurately described by this conventional modeling method. Nowadays, artificial intelligence technology is undergoing flourishing development and a large number of researchers have focused on machine learning and deep learning in various fields [15][16][17], which provides a novel modeling method for the MRB. In [18], the artificial neural network (ANN) was trained to predict the output torque based on the magnetic field intensity, which was measured by the embedded Hall sensors.…”

Section: Introductionmentioning

confidence: 99%

A novel modeling approach for the magnetorheological brake system based on improved LSTM

Lu,

Peng,

Hua

et al. 2024

Smart Mater. Struct.

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In order to establish the model of the MRB system under long-term operation and different working conditions, a novel performance prediction approach based on an improved LSTM model is proposed. The framework of the proposed approach is presented, and an improved sparrow search algorithm is designed to optimize the hyperparameters of LSTM. Moreover, the proposed prediction approach based on improved LSTM is designed and the flowchart of this approach is shown. In addition, the first simulation example was carried out to demonstrate the effectiveness of the proposed model compared with the ANN model and the conventional geometric model. Finally, another example was designed to exhibit the superior performance of the proposed algorithm compared with other algorithms.

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“…They can monitor the machine system state based on the requirements of an effective aero-engine prognostic and also the monitoring task has strong versatility. Chen [6] proposed an improved HI fusion method for generating a degradation tendency tracking strategy to predict the gear's RUL. Wang [7] extended the extreme learning machine to an interpretable neural network structure, which can automatically localize informative frequency bands and construct HI for machine condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

Machinery Prognostics and High-Dimensional Data Feature Extraction Based on a Transformer Self-Attention Transfer Network

Sun,

Peng,

Huang

2023

Sensors

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Machinery degradation assessment can offer meaningful prognosis and health management information. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain discriminator to extract invariant features from different machine operating conditions. Case studies were carried out using the FEMTO-ST bearing dataset, and the Monte Carlo method was employed for RUL prediction during the degradation process. When compared to other established techniques such as the RNN-based RUL prediction method, convolutional LSTM network, Bi-directional LSTM network with attention mechanism, and the traditional RUL prediction method based on vibration frequency anomaly detection and survival time ratio, our proposed TSTN method demonstrates superior RUL prediction accuracy with a notable SCORE of 0.4017. These results underscore the significant advantages and potential of the TSTN approach over other state-of-the-art techniques.

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A generalized degradation tendency tracking strategy for gearbox remaining useful life prediction

Cited by 10 publications

References 30 publications

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

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

A novel modeling approach for the magnetorheological brake system based on improved LSTM

Machinery Prognostics and High-Dimensional Data Feature Extraction Based on a Transformer Self-Attention Transfer Network

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