Energies

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21 pages, 6051 KiB

Open AccessArticle

Model Based IAS Analysis for Fault Detection and Diagnosis of IC Engine Powertrains

by Yuandong Xu, Baoshan Huang, Yuliang Yun, Robert Cattley, Fengshou Gu and Andrew D. Ball

Energies 2020, 13(3), 565; https://doi.org/10.3390/en13030565 - 24 Jan 2020

Cited by 9 | Viewed by 3195

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain [...] Read more.

Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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14 pages, 1025 KiB

Open AccessFeature PaperArticle

Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning

by Jinxin Wang, Zhongwei Wang, Xiuzhen Ma, Guojin Feng and Chi Zhang

Energies 2020, 13(2), 310; https://doi.org/10.3390/en13020310 - 8 Jan 2020

Cited by 1 | Viewed by 2032

Abstract

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been [...] Read more.

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been spent on the optimization of sensor location in a complex engineering system, which is expected to be a critical step for the successful application of fault diagnostics. In this paper, a novel sensor location approach is proposed for the purpose of fault isolation using population-based incremental learning (PBIL). A directed graph is used to model the fault propagation of a complex engineering system. The multidimensional causal relationships of faults and symptoms were obtained via traversing the directed path in the directed graph. To locate the minimal quantity of sensors for desired fault isolatability, the problem of sensor location was firstly formulated as an optimization problem and then handled using PBIL. Two classical cases, including a diesel engine and a fluid catalytic cracking unit (FCCU), were taken as examples to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can minimize the quantity of sensors while keeping the capacity of fault isolation unchanged. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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19 pages, 10188 KiB

Open AccessArticle

Autocorrelation Ensemble Average of Larger Amplitude Impact Transients for the Fault Diagnosis of Rolling Element Bearings

by Lei Hu, Yuandong Xu, Fengshou Gu, Jing He, Niaoqing Hu and Andrew Ball

Energies 2019, 12(24), 4740; https://doi.org/10.3390/en12244740 - 12 Dec 2019

Cited by 4 | Viewed by 2455

Abstract

Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when [...] Read more.

Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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20 pages, 8373 KiB

Open AccessArticle

The Influences of Gradual Wears and Bearing Clearance of Gear Transmission on Dynamic Responses

by Ruiliang Zhang, Kaida Wang, Yandong Shi, Xiuquan Sun, Fengshou Gu and Tie Wang

Energies 2019, 12(24), 4731; https://doi.org/10.3390/en12244731 - 11 Dec 2019

Cited by 9 | Viewed by 2654

Abstract

Gears are important components of the transmission system. Tooth wear and bearing clearance are significant factors affecting the dynamics of the gear system. In order to reveal the effects of gradual wears and bearing clearance on the gear system dynamics, a six-degrees-of-freedom bending-torsion [...] Read more.

Gears are important components of the transmission system. Tooth wear and bearing clearance are significant factors affecting the dynamics of the gear system. In order to reveal the effects of gradual wears and bearing clearance on the gear system dynamics, a six-degrees-of-freedom bending-torsion coupled model of gear-rotor-bearing which considers surface wear, bearing clearance and backlash is established. The Rung-Kutta method is used to solve the nonlinear dynamic system, and the dynamic responses of the system are obtained. The results show that the time-varying mesh stiffness decreases with the tooth surface from the unworn phase to severe wear phase. At the same time, the change of the mesh stiffness in the double-tooth mesh area and single-tooth area are different. Moreover, the amplitude of the X-displacement, Y-displacement and relative gear mesh displacement will be enlarged slightly with the increase of wear depth or bearing clearance. By analyzing variation tendency in the frequency domain, the different order harmonics show the different change characteristic with the variation of the wear phases or bearing clearances. This study provides a theoretical basis for improving the transmission performance and the selection of the bearing clearances in the gear system. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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17 pages, 2870 KiB

Open AccessArticle

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

by Yunpeng Cao, Xinran Lv, Guodong Han, Junqi Luan and Shuying Li

Energies 2019, 12(24), 4701; https://doi.org/10.3390/en12244701 - 10 Dec 2019

Cited by 6 | Viewed by 2652

Abstract

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of [...] Read more.

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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18 pages, 3150 KiB

Open AccessArticle

A Marine Gas Turbine Fault Diagnosis Method Based on Endogenous Irreversible Loss

by Yunpeng Cao, Junqi Luan, Guodong Han, Xinran Lv and Shuying Li

Energies 2019, 12(24), 4677; https://doi.org/10.3390/en12244677 - 9 Dec 2019

Cited by 3 | Viewed by 2223

Abstract

When a malfunction occurs in a marine gas turbine, its thermal efficiency will decrease slightly, and the gas path fault is often difficult to distinguish. In order to solve this problem, based on the second law of thermodynamics, the endogenous irreversible loss (EIL) [...] Read more.

When a malfunction occurs in a marine gas turbine, its thermal efficiency will decrease slightly, and the gas path fault is often difficult to distinguish. In order to solve this problem, based on the second law of thermodynamics, the endogenous irreversible loss (EIL) model of the marine gas turbine is established, and the exergy loss analysis under normal conditions is carried out to verify the accuracy of the model. The fault diagnosis of gas turbine gas path based on EIL is proposed, and a simulation experiment conducted on a three-shaft marine gas turbine demonstrated that the proposed approach can detect and isolate gas path fault accurately under different operating conditons and enviroments. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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11 pages, 4168 KiB

Open AccessArticle

The Use of Acoustic Emission to Diagnosis of Fuel Injection Pumps of Marine Diesel Engines

by Artur Bejger and Jan Bohdan Drzewieniecki

Energies 2019, 12(24), 4661; https://doi.org/10.3390/en12244661 - 8 Dec 2019

Cited by 14 | Viewed by 8622

Abstract

The article draws attention to the problems of maintaining fuel injection pumps of marine diesel engines in the conditions of the use of residual fuels in which the quality is steadily deteriorating. The analysis of tribological processes occurring in hydraulic precision pairs of [...] Read more.

The article draws attention to the problems of maintaining fuel injection pumps of marine diesel engines in the conditions of the use of residual fuels in which the quality is steadily deteriorating. The analysis of tribological processes occurring in hydraulic precision pairs of fuel injection pumps, such as a barrel-plunger, is presented. Problems occurring regarding the operation of injection pumps and the possibilities of their avoidance on board are discussed. The means of condition monitoring, including the application of thermography methods, are characterized. The authors have done research concerning diagnosing injection systems of high and medium power engines by using acoustic emission (AE) signals. The experiment results obtained with the use of acoustic emission and wavelet analysis confirmed the dependency of the frequency components contained in the acoustic emission signal on the condition state of the injector pumps’ tribological pair. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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18 pages, 9353 KiB

Open AccessArticle

Optimization of Galloping Piezoelectric Energy Harvester with V-Shaped Groove in Low Wind Speed

by Kaiyuan Zhao, Qichang Zhang and Wei Wang

Energies 2019, 12(24), 4619; https://doi.org/10.3390/en12244619 - 5 Dec 2019

Cited by 37 | Viewed by 3676

Abstract

A square cylinder with a V-shaped groove on the windward side in the piezoelectric cantilever flow-induced vibration energy harvester (FIVEH) is presented to improve the output power of the energy harvester and reduce the critical velocity of the system, aiming at the self-powered [...] Read more.

A square cylinder with a V-shaped groove on the windward side in the piezoelectric cantilever flow-induced vibration energy harvester (FIVEH) is presented to improve the output power of the energy harvester and reduce the critical velocity of the system, aiming at the self-powered supply of low energy consumption devices in the natural environment with low wind speed. Seven groups of galloping piezoelectric energy harvesters (GPEHs) were designed and tested in a wind tunnel by gradually changing the angle of two symmetrical sharp angles of the V-groove. The GPEH with a sharp angle of 45° was selected as the optimal energy harvester. Its output power was 61% more than the GPEH without the V-shaped groove. The more accurate mathematical model was made by using the sparse identification method to calculate the empirical parameters of fluid based on the experimental data and the theoretical model. The critical velocity of the galloping system was calculated by analyzing the local Hopf bifurcation of the model. The minimum critical velocity was 2.53 m/s smaller than the maximum critical velocity at 4.69 m/s. These results make the GPEH with a V-shaped groove (GPEH-V) more suitable to harvest wind energy efficiently in a low wind speed environment. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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16 pages, 3124 KiB

Open AccessArticle

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

by Haiyang Li, Zuolu Wang, Dong Zhen, Fengshou Gu and Andrew Ball

Energies 2019, 12(23), 4437; https://doi.org/10.3390/en12234437 - 21 Nov 2019

Cited by 18 | Viewed by 3716

Abstract

Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole [...] Read more.

Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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14 pages, 1129 KiB

Open AccessArticle

Detection of Low Electrolyte Level for Vented Lead–Acid Batteries Based on Electrical Measurements

by Eugenio Camargo, Nancy Visairo, Ciro Núñez, Juan Segundo, Juan Cuevas and Dante Mora

Energies 2019, 12(23), 4435; https://doi.org/10.3390/en12234435 - 21 Nov 2019

Cited by 2 | Viewed by 4660

Abstract

It is well known that a low level of electrolytes in batteries produces a malfunction or even failure and irreversible damage. There are several kinds of sensors to detect the electrolyte level. Some of them are non-invasive, such as optical sensors of level, [...] Read more.

It is well known that a low level of electrolytes in batteries produces a malfunction or even failure and irreversible damage. There are several kinds of sensors to detect the electrolyte level. Some of them are non-invasive, such as optical sensors of level, while some others are invasive; but both require one sensor per battery. This paper proposes a different approach to detect the low electrolyte level, which neither requires invasive sensors nor one sensor for each battery. The approach is based on the estimation of the internal resistance of an equivalent electrical circuit (EEC) model of the battery. To establish the detection criterion of the low level of electrolytes, a statistical analysis is proposed. To demonstrate the feasibility of this approach to be considered a valid method, multiple experiments were performed. The experiments consisted of determining how the internal resistance is affected at eight different levels of electrolyte at different aging levels of vented lead–acid (VLA) batteries. The results have demonstrated the feasibility of this approach. Hence, this approach has the potential to be used for the reducing of sensors and avoiding invasive methods to determine the low level of electrolytes. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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15 pages, 4880 KiB

Open AccessArticle

Effects of Bounded Uncertainties on the Dynamic Characteristics of an Overhung Rotor System with Rubbing Fault

by Chao Fu, Dong Zhen, Yongfeng Yang, Fengshou Gu and Andrew Ball

Energies 2019, 12(22), 4365; https://doi.org/10.3390/en12224365 - 15 Nov 2019

Cited by 9 | Viewed by 3288

Abstract

This paper investigated the nonlinear vibrations of an uncertain overhung rotor system with rub-impact fault. As the clearance of the rotor and stator is getting smaller, contact between them often occurs at high rotation speeds. Meanwhile, inherent uncertainties in the rubbing can be [...] Read more.

This paper investigated the nonlinear vibrations of an uncertain overhung rotor system with rub-impact fault. As the clearance of the rotor and stator is getting smaller, contact between them often occurs at high rotation speeds. Meanwhile, inherent uncertainties in the rubbing can be introduced for a variety of reasons, and they are typically restricted to small-sample variables. It is important to gain a robust understanding of the dynamics of such a system under non-probabilistic uncertainties. A non-intrusive uncertainty quantification scheme, coupled with the Runge-Kutta method, was used to study the effects of the rub-impact related interval uncertainties on the dynamical response individually and simultaneously, including the uncertainties in the contact stiffness, clearance, and friction coefficient. Moreover, the numerical validation of the developed analysis method was verified through comparisons with the scanning approach. The results obtained provide some guidance for investigating the uncertain dynamics of rubbing rotors and diagnosing the rub-impact fault under non-random uncertainty. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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27 pages, 10586 KiB

Open AccessArticle

Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

by Binqiang Chen, Qixin Lan, Yang Li, Shiqiang Zhuang and Xincheng Cao

Energies 2019, 12(18), 3536; https://doi.org/10.3390/en12183536 - 15 Sep 2019

Cited by 7 | Viewed by 3705

Abstract

Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal [...] Read more.

Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal wave does not satisfy the condition of full period sampling, an effect of severe end distortion (SED), in the form of impulsive features, is likely to occur because of boundary extensions in discrete wavelet decompositions. The SED effect will complicate the extraction of weak fault features if it is left untreated. In this paper, we investigate the mechanism of the SED effect using theories based on Fourier analysis and wavelet analysis. To enhance feature extraction performance from displacement signals in the presence of strong sinusoidal waves, a novel method, based on the Fourier basis and a compound wavelet dictionary, is proposed. In the procedure, ratio-based spectrum correction methods, using the rectangle window as well as the Hanning window, are employed to obtain an optimized reduction of strong sinusoidal waves. The residual signal is further decomposed by the compound wavelet dictionary which consists of dyadic wavelet packets and implicit wavelet packets. It was verified through numerical simulations that the reconstructed signal in each wavelet subspace can avoid severe end distortions. The proposed method was applied to case studies of an experimental test with rub impact fault and an engineering test with blade crack fault. The analysis results demonstrate the proposed method can effectively suppress the SED effect in displacement signal analysis, and therefore enhance the performance of wavelet analysis in extracting weak fault features. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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20 pages, 3514 KiB

Open AccessArticle

Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

by Zuolu Wang, Jie Yang, Haiyang Li, Dong Zhen, Yuandong Xu and Fengshou Gu

Energies 2019, 12(17), 3279; https://doi.org/10.3390/en12173279 - 26 Aug 2019

Cited by 26 | Viewed by 3548

Abstract

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous [...] Read more.

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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15 pages, 4524 KiB

Open AccessArticle

Electrical Treeing in Power Cable Insulation under Harmonics Superimposed on Unfiltered HVDC Voltages

by Mehrtash Azizian Fard, Mohamed Emad Farrag, Alistair Reid and Faris Al-Naemi

Energies 2019, 12(16), 3113; https://doi.org/10.3390/en12163113 - 14 Aug 2019

Cited by 19 | Viewed by 4582

Abstract

Insulation degradation is an irreversible phenomenon that can potentially lead to failure of power cable systems. This paper describes the results of an experimental investigation into the influence of direct current (DC) superimposed with harmonic voltages on both partial discharge (PD) activity and [...] Read more.

Insulation degradation is an irreversible phenomenon that can potentially lead to failure of power cable systems. This paper describes the results of an experimental investigation into the influence of direct current (DC) superimposed with harmonic voltages on both partial discharge (PD) activity and electrical tree (ET) phenomena within polymeric insulations. The test samples were prepared from a high voltage direct current (HVDC) cross linked polyethylene (XLPE) power cable. A double electrode arrangement was employed to produce divergent electric fields within the test samples that could possibly result in formation of electrical trees. The developed ETs were observed via an optical method and, at the same time, the emanating PD pulses were measured using conventional techniques. The results show a tenable relation between ETs, PD activities, and the level of harmonic voltages. An increase in harmonic levels has a marked effect on development of electrical trees as the firing angle increases, which also leads to higher activity of partial discharges. This study of the influencing operational parameters of HVDC converters on power cable insulation is predicted to contribute to enhancements in cable design and progressive advancement in condition monitoring and insulation diagnostic techniques that can lead to more effective asset management in HVDC systems. Full article

(This article belongs to the Special Issue Diagnostics and Condition Monitoring Technologies for Assuring Asset Performances)

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