A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurements

Ahmed, Umair; Ali, Fakhre; Jennions, Ian K.

doi:10.1016/j.paerosci.2021.100721

Cited by 30 publications

(19 citation statements)

References 81 publications

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“…The aircraft auxiliary power unit (APU) is a turboshaft engine [1], composed of several rotating components, valves, guide vanes, igniters, and other associated accessories to generate the required electrical and pneumatic power for aircraft systems. An APU is a complex piece of machinery that requires continuous monitoring to ensure its performance and reliability.…”

Section: A Backgroundmentioning

confidence: 99%

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

2022

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This paper presents a comprehensive evaluation of the near-field acoustics of an aircraft auxiliary power unit (APU), based on experimental data acquired from an in-situ APU. The aim is to establish whether near-field acoustics can be implemented for online condition monitoring. The APU of Cranfield University's demonstrator aircraft, a Boeing 737-400, has been instrumented to acquire acoustics (near-field and far-field) and vibration data in synchronization with aircraft state parameters under a wide range of operating conditions. The acquired data is first implemented to determine the efficacy of employing nearfield / far-field microphones, and vibration sensors, to monitor the combustion noise and tonal frequency levels from the APU components. Subsequently, an evaluation of the broadband characteristics of the vibroacoustic data and its variations against APU states and performance parameters is conducted based on several categories of feature extraction techniques. The findings suggest that nearfield acoustics lacks the ability to capture the combustion noise process. In addition, the tonal frequencies are also lost due to the level of background noise, fluctuations in the APU speeds, and scattering effects. For the same reasons, the phase couplings occurring between the signals generated by the APU components cannot be detected using acoustic data. Nevertheless, the overall analysis substantiates that the near-field acoustic data can be used to predict the APU operating states and has the potential to be implemented for developing APU performance parameter estimation models to enable condition monitoring.

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Section: A Backgroundmentioning

confidence: 99%

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

2022

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“…Diagnosis strategy of nonstationary (transient) states of direct current in electric power systems of aircraft [20]. In [21], a fault diagnosis method using acoustic signature of aircraft auxiliary power unit.…”

Section: Introductionmentioning

confidence: 99%

Health management using fault detection and fault tolerant control of multicellular converter applied in more electric aircraft system

Mahboub¹,

Rouabah²,

Kafi³

et al. 2022

Diagnostyka

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The increased cost of fuel and maintenance in aircraft system lead to the concept of more electric aircraft, moreover this concept increase the use of power electronic converters in aircraft power system. Since in this application, the reliability is a crucial feature. Therefore, the use of more efficient, reliable and robust power converter with health management capability will be a big challenge. Multicellular topology of power converters has the required performance in terms of efficiency and robustness. However, the increased complexity of control and more power components (power switches and capacitors) goes along with an increase in possibility of failure in multicellular topology. Therefore, the main contribution of this paper is the use of multicellular topology advantageous with fault diagnosis and fault tolerant control in order to increase the robustness reliability. The health management using a fault detection with Fuzzy Pattern Matching (FPM) algorithm when a failure in power switches or flying capacitors of multicellular converter and a Fault Tolerant Control (FTC) with sliding mode of second parallel three cells multicellular converters. Simulation results with Matlab show the increased efficiency and the continuity of work during failure mode in aircraft power system.

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“…To avoid critical damage and abrupt stopping of machine operation, rotating machinery failures should be detected as early as possible [7]. Failures can cause operational delays and enormous financial losses [8].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, by monitoring the mapping relation in multi-dimensional space, additional hidden layers provide a more precise distribution among network input and output, improving the model's capacity to deal with nonlinear complex features. The listed formulas (8)(9)(10)(11)(12) illustrate the update rules for each parameter following the use of LSTM and the attention mechanism:…”

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confidence: 99%

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

et al. 2021

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Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method's applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

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A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurements

Cited by 30 publications

References 81 publications

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

Evaluation of Aircraft Auxiliary Power Unit Near-Field Acoustics for Condition Monitoring

Health management using fault detection and fault tolerant control of multicellular converter applied in more electric aircraft system

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

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