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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive

Jiantao Lu1, Kuangzhi Yang1, Peng Zhang2

  • 1College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, China.

Sensors (Basel, Switzerland)
|April 12, 2025
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Summary
This summary is machine-generated.

This study introduces an enhanced Slice-level Adaptive Normalization (SAN) with Long Short-Term Memory (LSTM) for aircraft engine trend forecasting. The method improves prediction accuracy for early anomaly warnings, enhancing safety.

Keywords:
L1 filteringLSTMenhanced SANmulti-operating conditiontrend forecasting

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Area of Science:

  • Aerospace Engineering
  • Mechanical Engineering
  • Data Science

Background:

  • Aircraft engine failures pose significant safety risks.
  • Accurate trend forecasting and early anomaly detection are crucial for preventing accidents.
  • Existing methods may struggle with non-stationary data and local overfitting.

Purpose of the Study:

  • To propose an advanced trend forecasting method for aircraft engines.
  • To enhance the accuracy and reliability of early anomaly warnings.
  • To address limitations of current normalization and forecasting techniques.

Main Methods:

  • Developed a condition recognition technology to classify operating states (idling, starting, utmost).
  • Applied an enhanced Slice-level Adaptive Normalization (SAN) with L1 filtering to handle non-stationary signals and reduce overfitting.
  • Integrated an L1 filter for trend extraction and quantitatively estimated slice length with tail amendment.
  • Constructed a Long Short-Term Memory (LSTM) neural network for forecasting normalized data.

Main Results:

  • The proposed method demonstrated higher forecasting accuracy compared to existing approaches.
  • Effective reduction of signal fluctuations, noise, and overfitting was achieved.
  • The enhanced SAN method successfully alleviated non-stationary factors in vibration signals.
  • The tail amendment technology expanded the applicable range of the normalization technique.

Conclusions:

  • The enhanced SAN-LSTM method provides a robust approach for aircraft engine trend forecasting.
  • The technique offers improved accuracy for early anomaly detection, contributing to enhanced aviation safety.
  • This method is effective across multiple operating conditions, showing its versatility.