Study on signal recognition and diagnosis for spacecraft based on deep learning method

Li, Ke; Wang, Quanxin

doi:10.1109/phm.2015.7380040

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Some recent work in deep learning for CM looked at Generative Adversarial Network designs, in which one network tried to mimic a signal that competes with another network that tries to distinguish real signals from the generated ones [21]. Machine learning (ML) approaches for fault detection and diagnosis of complex machines have been explored in [1,25]; deep learning in particular has been applied to fault diagnosis in, e.g., [26][27][28][29].…”

Section: Condition Monitoring In Electric Motorsmentioning

confidence: 99%

Condition Monitoring and Fault Detection in Small Induction Motors Using Machine Learning Algorithms

Sobhi,

Reshadi,

Zarft

et al. 2023

Information

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Electric induction motors are one of the most important and widely used classes of machines in modern industry. Large motors, which are commonly process-critical, will usually have built-in condition-monitoring systems to facilitate preventive maintenance and fault detection. Such capabilities are usually not cost-effective for small (under ten horsepower) motors, as they are inexpensive to replace. However, large industrial sites may use hundreds of these small motors, often to drive cooling fans or lubrication pumps for larger machines. Multiple small motors may further be assigned to a single electrical circuit, meaning a failure in one could damage other motors on that circuit. There is thus a need for condition monitoring of aggregations of small motors. We report on an ongoing project to develop a machine-learning-based solution for fault detection in multiple small electric motors. Shallow and deep learning approaches to this problem are investigated and compared, with a hybrid deep/shallow system ultimately being the most effective.

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Section: Condition Monitoring In Electric Motorsmentioning

confidence: 99%

Condition Monitoring and Fault Detection in Small Induction Motors Using Machine Learning Algorithms

Sobhi,

Reshadi,

Zarft

et al. 2023

Information

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“…Neural network technology's nonlinear capability has developed into a useful tool for fusing data from several sensors effectively and quickly. To increase the precision and accuracy of multi-sensor data processing, References [66,67] made use of rough sets and backpropagation networks. Reference [68] suggested a multi-sensor data fusion approach based on belief entropy and belief-based divergence measures.…”

Section: Data Fusionmentioning

confidence: 99%

Prospects and Challenges of AI and Neural Network Algorithms in MEMS Microcantilever Biosensors

Wang

Shi

et al. 2022

Processes

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This paper focuses on the use of AI in various MEMS (Micro-Electro-Mechanical System) biosensor types. Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into our daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a number of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active development of artificial intelligence (AI) and neural networks. As a result, this paper examines the fundamentals of the neural algorithm and goes into great detail on the fundamentals and uses of the principal component analysis approach. A neural algorithm application in Micro-Electro-Mechanical System microcantilever biosensors is anticipated through the associated application of the principal com-ponent analysis approach. Our investigation has more scientific study value, because there are currently no favorable reports on the market regarding the use of AI with Micro-Electro-Mechanical System microcantilever sensors. Focusing on AI and neural networks, this paper introduces Micro-Electro-Mechanical System biosensors using artificial intelligence, which greatly promotes the development of next-generation intelligent sensing systems, and the potential applications and prospects of neural networks in the field of microcantilever biosensors.

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“…Among these, the autoclave process is particularly interesting due to its higher percentage of vinyl acetate content, enhancing EVA applications' versatility. EVA production in the autoclave process using an autoclave reactor has several problems that can occur in autoclave reactors, such as leaks in valves, overpressure, explosions due to excessive exothermic reactions, bearing damage, and others [1][2][3][4][5][6][7][8][9]. One of the critical problems that occurs in autoclave reactors is bearing damage.…”

Section: Introductionmentioning

confidence: 99%

“…Counter-flow and saw-tooth impellers produced uniform flows, whereas anchor and Rushton impellers focused flow in specific areas, producing higher product quality. Furthermore, studies in the literature [1][2][3][4][5][6][7][8] have discussed common problems in autoclave reactors, such as bearing damage, abnormal pressures and temperatures, and leaks. A critical problem in autoclave reactors is damage to the bearings.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Autoclave Reactors to Improve Bearing Life Using the Taguchi Method and the Response Surface Methodology

Fariz,

Patel,

Chiu

et al. 2023

Inventions

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Plastic pervasiveness in daily life has increased in tandem with population growth. Ethylene–vinyl acetate (EVA) is emerging as a popular compound for manufacturing plastic, which is obtained from ethylene and vinyl acetate synthesis. EVA is produced using autoclave reactors, which often encounter bearing damage under specific operating conditions. This research aims to optimize the parameters in autoclave reactors to enhance bearing life. The study focuses on two crucial factors: the number of impellers and the temperature, with bearing life as the response variable. Simulations using finite-element analysis were conducted to obtain the fatigue life of bearings and validated using real-time company data stating the damage of bearings within 80 days. The optimization process employed the Taguchi method (TM) and the response surface methodology (RSM). A comparison of these techniques revealed that temperature had the most significant influence on the response. Interestingly, both methods yielded the same optimal parameters: seven impellers and a temperature of 150 °C. The simulation results using these optimized parameters demonstrated a noteworthy 3.095% increase in bearing life compared to the initial design. The RSM outperformed the Taguchi method in accurately predicting response values with minimum prediction error under optimal conditions.

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Study on signal recognition and diagnosis for spacecraft based on deep learning method

Cited by 8 publications

References 8 publications

Condition Monitoring and Fault Detection in Small Induction Motors Using Machine Learning Algorithms

Condition Monitoring and Fault Detection in Small Induction Motors Using Machine Learning Algorithms

Prospects and Challenges of AI and Neural Network Algorithms in MEMS Microcantilever Biosensors

Optimization of Autoclave Reactors to Improve Bearing Life Using the Taguchi Method and the Response Surface Methodology

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