Dual fault warning method for coal mill based on Autoformer WaveBound

Huang, Congzhi; Qu, Shuangyan; Ke, Zhiwu; Zheng, Wei

doi:10.1016/j.ress.2024.110030

Cited by 4 publications

(4 citation statements)

References 50 publications

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“…Whilst our research has made significant strides in the field of industrial equipment fault warning, we recognise that as industrial technology continues to evolve and the types of equipment and fault patterns diversify, handling complex and diverse data will pose a challenge [4,36]. Our model needs to have stronger adaptability and generalisation capabilities.…”

Section: Discussionmentioning

confidence: 99%

“…To distinguish these algorithms, we named them as HPOPS, HPOPW, HPOSS, and HPORS, respectively. Additionally, we adopted the method of Tian et al [33], using the relative percentage deviation (RPD) as a metric to measure the performance of each algorithm, as shown in Equation (36). Clearly, the smaller the RPD, the better the performance of a particular algorithm.…”

Section: Analysis Of Improvement Strategiesmentioning

confidence: 99%

“…Alg Sol − Min Sol Min Sol (36) where Min Sol represents the minimum value of the fitness function; Alg Sol denotes the value of the fitness function obtained in each experiment.…”

Section: Rpd =mentioning

confidence: 99%

“…Finally, the results are shown in Table 5 and Figure 7. performance of each algorithm, as shown in Equation (36). Clearly, the smaller the RPD, the better the performance of a particular algorithm.…”

Section: Rpd =mentioning

confidence: 99%

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Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

Tian,

Zhang,

Zheng

et al. 2024

Mathematics

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In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Analysis Of Improvement Strategiesmentioning

confidence: 99%

“…Alg Sol − Min Sol Min Sol (36) where Min Sol represents the minimum value of the fitness function; Alg Sol denotes the value of the fitness function obtained in each experiment.…”

Section: Rpd =mentioning

confidence: 99%

Section: Rpd =mentioning

confidence: 99%

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Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

Tian,

Zhang,

Zheng

et al. 2024

Mathematics

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Early Warning of Energy Storage Battery Fault Based on Improved Autoformer and Adaptive Threshold

Cheng,

Zhang,

Liu

2024

Energy Tech

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To enhance voltage prediction accuracy in energy storage batteries and address the limitations of fixed threshold warning methods, a fault warning approach based on an improved Autoformer model and adaptive thresholds is proposed. First, a spatiotemporal filtering layer is introduced into the autocorrelation mechanism to analyze the trend features of voltage sequences across different frequency domains. Additionally, an adaptive gating residual connection is used to link the sublayer and current layer output features, which helps to improve the model's adaptive feature selection capability. This innovation enables the development of a robust voltage prediction model based on the enhanced Autoformer. Then, a similarity‐based adaptive threshold, using interval estimation, is employed to rapidly track variations in battery voltage, enabling dynamic adjustment of voltage thresholds. Finally, the proposed method is validated with real voltage data from an operational energy storage station. The experimental results shows that the proposed model has higher accuracy and robustness compared to similar methods. The adaptive threshold can reduce the false alarm rate by ≈18% and issue alarms at three sampling points ahead of the battery management system alarm, improving fault warning accuracy and illustrating that early fault warning is effectively and practically carried out using the method.

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A novel hybrid strategy for damage detection of wind turbine yaw bearing

Wang,

Jin,

et al. 2024

Structural Health Monitoring

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Compared with the strong background noise, the impact characteristics of wind turbine yaw bearing are weak in the initial damage stage. In order to completely eliminate unnecessary noise and detect damage characteristics, the useful component should be extracted from the complex vibration signal. To solve the problem that existing methods cannot effectively extract the useful component, an innovative hybrid strategy fusing weighted dual tree complex wavelet packet transform (WDTCWPT) with optimized minimum noise amplitude deconvolution (OMNAD) is developed. First, the envelope variable Rényi entropy indicator is defined, which reflects the impact strength. Meanwhile, this indicator is used to determine the optimal number of WDTCWPT decomposition level, and the weighted wavelet coefficient reconstruction is executed to get the enhanced signal. Subsequently, a square envelope correlation L-Kurtosis (SECLK) indicator is further defined and SECLK spectrum is utilized for estimating the deconvolution period. Ulteriorly, the multiple grid search algorithm is designed to automatically determine the optimal filter length as well as the noise ratio. And the optimal deconvolution signal with obvious damage characteristics can be obtained by OMNAD. Finally, the damage type of yaw bearing can be judged by performing envelope spectrum analysis on the deconvolution signal. The detection results of simulation and experiment signals demonstrate this developed hybrid strategy can accurately detect the local damage of yaw bearing under strong interference environment, which has certain engineering application value.

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Dual fault warning method for coal mill based on Autoformer WaveBound

Cited by 4 publications

References 50 publications

Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

Early Warning of Energy Storage Battery Fault Based on Improved Autoformer and Adaptive Threshold

A novel hybrid strategy for damage detection of wind turbine yaw bearing

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