A Non-Probabilistic Metric Derived From Condition Information for Operational Reliability Assessment of Aero-Engines

Sun, Chuang; He, Zhengjia; Cao, Hongrui; Zhang, Zhousuo; Zuo, Ming J.

doi:10.1109/tr.2014.2336032

Cited by 38 publications

(1 citation statement)

References 62 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the primary goals of this research is to properly combine vibration sensor data with thermal sensor data to build a reliable PHM scheme. To achieve this integration, appropriate fusion techniques must be used to develop strong health indicators (HIs) for an efficient diagnostic model [37][38][39]. It is critical to note that, while the requirement for a fusion algorithm is undeniable, the technique to be used is significantly dependent on the specific challenge at hand.…”

Section: Introductionmentioning

confidence: 99%

Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach

Lee,

Okwuosa,

Hur

2023

Inventions

View full text Add to dashboard Cite

In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction. The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (AE-LSTM) model, to ensure that all important characteristics of the system are utilized. Fast independent component analysis (FastICA) is employed to fuse the data signals from both sensors while retaining their characteristics. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the AE-LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. The study used robust global metric evaluation techniques, such as accuracy, F1-score, and confusion metrics, to thoroughly evaluate the model’s dependability and efficiency. LSTM was additionally employed for anomaly detection to further emphasize the adaptability and interoperability of the methodology. This modification yielded a remarkable accuracy of 89.67%, offering additional validation of the model’s reliability and competence.

show abstract

Section: Introductionmentioning

confidence: 99%

Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach

Lee,

Okwuosa,

Hur

2023

Inventions

View full text Add to dashboard Cite

show abstract

Intelligent Data Analysis to Calculate the Operational Reliability Coefficient

Tabares

Campos

Silva

et al. 2018

Progress in Artificial Intelligence and Pattern Recognition

View full text Add to dashboard Cite

A deep learning-based conditional system health index method to reduce the uncertainty of remaining useful life prediction

Jang

2022

Soft Comput

View full text Add to dashboard Cite

Many recent data-driven studies have used sensor profile data for prognostics and health management (PHM). However, existing data-driven PHM techniques are vulnerable to three types of uncertainty: sensor noise inherent to the sensor profile data, uncertainty regarding the current health status diagnosis caused by monitoring a single health index (HI), and uncertainty in predicting the remaining useful life (RUL), which is affected by unpredictable changes in system operating conditions and the future external environment. This study proposes a deep conditional health index extraction network (DCHIEN) for PHM to effectively manage these three types of uncertainty. DCHIEN is a model that combines a stacked denoising autoencoder that extracts high-level features robust to sensor noise with a feed-forward neural network that produces an HI based on user-defined monitoring conditions. This approach supports system health monitoring using the conditional HI, as well as prognostics using RUL interval predictions. Extensive experiments were conducted using NASA's turbofan engine degradation dataset. The results show that the proposed method achieves a superior RUL prediction performance compared to stateof-the-art methods and that uncertainties can be effectively managed.

show abstract

A Non-Probabilistic Metric Derived From Condition Information for Operational Reliability Assessment of Aero-Engines

Cited by 38 publications

References 62 publications

Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach

Extruder Machine Gear Fault Detection Using Autoencoder LSTM via Sensor Fusion Approach

Intelligent Data Analysis to Calculate the Operational Reliability Coefficient

A deep learning-based conditional system health index method to reduce the uncertainty of remaining useful life prediction

Contact Info

Product

Resources

About