A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions

Bender, Amelie

doi:10.3390/machines9100210

Cited by 4 publications

(4 citation statements)

References 58 publications

(93 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Denote the ith tree forecast as T i (x). The ultimate prediction of the Random Forest ensemble is determined by Equation (11). Ŷ(x) = mode{T 1 (x), T 2 (x), .…”

Section: Random Forest Classifiermentioning

confidence: 99%

“…. , T n (x)} (11) where x represents the input data point. Random Forest stands out as a versatile and potent tool, significantly enhancing the reliability and efficiency of predictive maintenance strategies across diverse industrial applications [26].…”

Section: Random Forest Classifiermentioning

confidence: 99%

“…More research has been conducted in the recent literature on fault prognosis, which includes estimating remaining useful life (RUL) and projecting future states [10]. One frequently used prognostic method revolves around particle filters, especially when it comes to forecasting the RUL of rotary systems experiencing problems like cracks [11]. Moreover, similarity-based prognostic techniques play a role in estimating RUL.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Hussain,

Zaidi

2024

Applied Sciences

View full text Add to dashboard Cite

This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.

show abstract

“…Denote the ith tree forecast as T i (x). The ultimate prediction of the Random Forest ensemble is determined by Equation (11). Ŷ(x) = mode{T 1 (x), T 2 (x), .…”

Section: Random Forest Classifiermentioning

confidence: 99%

Section: Random Forest Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Hussain,

Zaidi

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…It uses sequential importance sampling to provide an approximate solution to the Bayesian optimal solution and is suitable for describing a nonlinear degradation process with non-Gaussian random noise. There are three main ideas aiming to improve the life prediction accuracy of the PF method: the selection of an appropriate degradation model [101], the improvement of PF [103], and integration with other methods [104].…”

Section: State Of Research On Data-model-fusion-driven Rul Prediction...mentioning

confidence: 99%

Summarization of Remaining Life Prediction Methods for Special Power Plants

Liang,

Li,

Zhao

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.

show abstract

Model-based condition monitoring of piezoelectric bending actuators

Bender¹

2023

Sensors and Actuators A: Physical

View full text Add to dashboard Cite

A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions

Cited by 4 publications

References 58 publications

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Summarization of Remaining Life Prediction Methods for Special Power Plants

Model-based condition monitoring of piezoelectric bending actuators

Contact Info

Product

Resources

About