Component Faults Research Articles

Time-Synchronized Fault-Tolerant Control for Robotic Manipulators

This article proposes a time-synchronized fault-tolerant convergence control method for n-degree-of-freedom robotic manipulators. The main challenge lies in driving the tracking errors of all joints to converge simultaneously, especially in the presence of system faults, external disturbances, and model uncertainties. We introduce a normalized sign function that guarantees the property of ratio persistence for all joints and plays a crucial role in time-synchronized convergence control. A time-synchronized convergence observer is proposed that not only adopts a time-synchronized convergence control framework but also overcomes the lumped uncertainty term, which includes the system fault components, external disturbances, and system uncertainties. A salient feature of this method is that, regardless of the initial state and various uncertainties, each component of the robot manipulator system can simultaneously converge to an equilibrium point. Simulations conducted on a two-link robotic manipulator demonstrate the notable benefits of the designed time-synchronized control method, as evidenced by the comparative results.

Concept Drift Early Fault Detection in Wind Turbine Based on Distance Metric: A Systematic Literature Review

The Supervisory Control and Data Acquisition (SCADA) system in wind turbines generates substantial data that remains underutilized in terms of wind farm operation and maintenance (O&M). Numerous fault detection methods leveraging SCADA data are being extensively researched to reduce O&M costs. The detection methods are revolutionizing wind farm O&M strategies, shifting from scheduled passive detection to predictive active detection, with the potential to significantly reduce spare parts and labor costs. This paper presents a systematic review of wind turbine fault detection methods based on concept drift and distance metrics, employing the PRISMA methodology. The selected literature is analyzed from three perspectives: fault components, modeling methods, and data sources. Additionally, this review addresses research questions related to current trends, concept drift applications, and distance metric utilization in wind turbine fault detection. Lastly, it provides valuable insights for researchers and industry practitioners in wind energy engineering to explore future research and development in fault detection techniques for enhancing the reliability and efficiency of wind turbine operations.

Geometry-Based Synchrosqueezing S-Transform with Shifted Instantaneous Frequency Estimator Applied to Gearbox Fault Diagnosis.

This paper introduces a novel geometry-based synchrosqueezing S-transform (GSSST) for advanced gearbox fault diagnosis, designed to enhance diagnostic precision in both planetary and parallel gearboxes. Traditional time-frequency analysis (TFA) methods, such as the Synchrosqueezing S-transform (SSST), often face challenges in accurately representing fault-related features when significant mode closely spaced components are present. The proposed GSSST method overcomes these limitations by implementing an intuitive geometric reassignment framework, which reassigns time-frequency (TF) coefficients to maximize energy concentration, thereby allowing fault components to be distinctly isolated even under challenging conditions. The GSSST algorithm calculates a new instantaneous frequency (IF) estimator that aligns closely with the ideal IF, thus concentrating TF coefficients more effectively than existing methods. Experimental validation, including tests on simulated signals and real-world gearbox fault data, demonstrates that GSSST achieves high robustness and diagnostic accuracy across various types of gearbox faults even in the presence of noise. Moreover, unlike conventional reassignment method, GSSST supports partial signal reconstruction, a key advantage for applications requiring accurate signal recovery. This research highlights GSSST as a promising and versatile tool for diagnosing complex mechanical faults and provides new insights for the future development of TFA methods in mechanical fault analysis.

Broken Rotor Bar Fault Detection in Induction Motor Based on Spectral Analysis

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

The SAMgram: a novel local enhancement and targeted extraction strategy for weak bearing fault characteristics

Envelope analysis is one of the most commonly used methods in rolling bearing fault diagnosis. However, when a signal contains heavy noise, even if an appropriate frequency band is selected, the fault information can still be overwhelmed. Unlike traditional use of spectral amplitude modulation, a novel SAMgram is proposed in this article for the enhancement and extraction of weak bearing fault characteristics in a signal, where local spectral amplitude modulation (LSAM) is performed to highlight bearing fault components and improve the proportion of fault information. Meanwhile, a targeted indicator called normalized harmonic kurtosis is proposed to select an optimal modified filtered signal automatically by quantifying repetitive transient characteristics. To extend LSAM to practical applications, two spectrum segmentation strategies are provided based on scanning spectrum and trend component, named the scanning SAMgram and adaptive SAMgram, respectively, which aim at determining the optimal modified filtered signal for demodulation by searching for the combinations of frequency bands and magnitude orders. A simulated signal of bearing compound faults and experimental signals of bearing outer ring and inner ring faults indicate that the proposed method can not only select a frequency band related to bearing defect to eliminate interference of invalid components but also highlight fault characteristics in the selected frequency band and weaken the disturbance of noise, which is superior over traditional envelope analysis-based methods and more applicable for fault diagnosis of rolling bearings under complex environments.

Adaptive fault components extraction based on physically interpretable optimized weight time–frequency matrix index and its applications in rotating machinery fault diagnosis

The adaptive fault component extraction (AFCE) process, crucial for diagnosing rotating machinery faults based on vibration signals, relies on statistical indices. Conventional indices like kurtosis and correlated kurtosis, sensitive to random noise, may produce imprecise results. To address this, this article proposes a new index—the physically interpretable optimized weight time–frequency matrix index (PIOWTFMI). Derived through large-margin convex optimization of time–frequency matrices from healthy and faulty samples, PIOWTFMI is not only interpretable but also enhances the separation of faulty components from interference. Additionally, an implementation method of AFCE based on PIOWTFMI is proposed, compatible with existing decomposition techniques like mode decomposition and requiring minimal parameter adjustments. Finally, the effectiveness of the method is verified by bearing public data from Intelligent Maintenance Systems (IMS) and industrial real truck transmission gear data. The results demonstrate the superior performance of proposed method compared to classical approaches such as fast kurtogram, feature mode decomposition, maximum correlation kurtosis deconvolution and multipoint optimized minimum entropy deconvolution adjusted.

Sliding Mode Fault-Tolerant Control for Nonlinear High-Order Fully Actuated Systems.

The high-order fully actuated systems (HOFASs) approach can only completely eliminate known nonlinearities. However, the practical systems often encounter unknown nonlinearities, including disturbances and faults. Therefore, this article studies a class of nonlinear HOFAS with component faults and disturbances. By applying the HOFAS theory, a novel integrated sliding mode fault-tolerant control strategy is proposed to ensure the stability of the closed-loop system. The state feedback and output feedback controllers are designed, respectively. For output feedback control, an extended state observer is designed to estimate system states. Once the HOFAS model is established, the designed controller and extended state observer can be directly implemented, facilitating the analysis and design of the control system. And the stability analysis does not depend on the complexity of the nonlinear functions. Finally, a numerical simulation example shows the effectiveness of the proposed method.

A novel noise reduction algorithm and its application in failure information extraction of bearing

The performance of rolling bearing can directly affect the reliability of a rotating machine. Due to the influence of noise and irrelevant components, the fault information is rather weak in a bearing fault, which adds to the difficulty in judgment of bearing state and identification of fault types. To solve the problem of heterogeneous feature information caused by noise jamming in rolling bearing failure, difficulty in extracting fault feature, and misdiagnosis and misjudgment, the paper has proposed a denoising algorithm according to the covariance matrix of a Hankel matrix and information entropy (Hankel-Cov-IE). First, covariance matrix of a Hankel matrix of signals is constructed, and fault signals are preliminarily denoised based on denoising capacity of the covariance matrix and its nature of combining global and local features. To address the selection of sensitive fault components, information entropy (IE) is taken advantage of to screen out the vectors containing abundant feature information from the covariance matrix. The filtered feature vectors are reconstructed in the way of equal weight and faults of rolling bearing are identified with the spectrum of signals reconstructed. The presented algorithm is compared with different typical methods. The results indicate that the presented algorithm can reduce noise more effectively and achieve extraction of bearing failure features more comprehensively and correctly, with correct judgment of fault types. The presented algorithm can correctly extract the feature frequency of bearing fault and recognize the types of faults, and is insensitive to sensor sites, speeds, and failure types of bearings.

Research on fault component extraction and fault type identification of rotating machinery based on MDSM and a novel convolutional neural network

Abstract Given the complexity and difficulty in extracting and recognizing multi-axis mechanical fault components, a method for fault extraction and identification based on the Multi-Axis Displacement Superposition Method (MDSM) and a Novel Convolutional Neural Network (NCNN) is proposed. In the proposed MDSM method, first, correlation analysis is used to determine the operational status of the mechanical system and to identify the location of faults in the multi-axis rotating mechanical system. Secondly, a simplified initial point selection process is introduced to segment the collected fault component. Subsequently, a signal superposition method with position offset correction is employed to perform position correction and superposition operations on the segmented signals, enhancing the accuracy of the fault signal. Finally, the front end of the superimposed signals is extracted as the fault component, completing the separation and extraction of the fault components. For the extracted fault signals, an NCNN is designed for fault-type identification. NCNN improves computational efficiency and effectively completes fault feature identification through a lightweight network architecture and a nonlinear learning rate scheduling strategy. The results of the experiment show that the proposed method can accurately determine the fault occurrence location, extract the fault components, and achieve high-accuracy fault type identification.

Application of FCEEMD-TSMFDE and adaptive CatBoost in fault diagnosis of complex variable condition bearings

The mode mixing problem and inherent mode function selection bias in Fast Ensemble Empirical Mode Decomposition (FEEMD) result in ineffective extraction of fault components during the denoising stage, the loss of coarse-grained information in Multiscale Fuzzy Dispersion Entropy (MFDE) reduces the stability of fault features, and the lack of adaptability of CatBoost hyperparameters leads to reduced diagnostic accuracy. Therefore, a complex variable operating condition fault diagnosis method based on Fast Complementary Ensemble Empirical Mode Decomposition (FCEEMD) - Time-shift Multiscale Fuzzy Dispersion Entropy (TSMFDE) and adaptive Optuna-CatBoost is proposed. We introduce paired white noise with opposite signs in the construction of FCEEMD, effectively suppressing mode aliasing by neutralizing the residual noise generated during decomposition. Then, the Maximum Information Coefficient / Gini Index was introduced to construct a composite screening strategy, retaining the Intrinsic Mode Function (IMF) components that are strongly correlated with the original signal and have a fault impact to reconstruct the denoised signal. Secondly, time-shift multiscale is introduced into the coarse-grained process, and the constructed TSMFDE effectively extracts complete and stable fault features. Finally, with the introduction of the Optuna hyperparameter optimization framework, the adaptive Optuna-CatBoost can accurately diagnose bearing faults. The average fault diagnosis accuracy of the proposed method reached 99.76% and 99.33%, indicating that FCEEMD based on white noise can quickly and accurately decompose non-aliasing vibration modes, and the composite screening strategy can further filter out irrelevant noise modes and improve signal quality; The proposed TSMFDE can extract stable fault features, and its combination with Optuna-CatBoost can further improve the accuracy of fault diagnosis. This model is expected to be applied in more fields of feature extraction and pattern recognition.

A cyber-physical framework for fault-tolerant engine speed control of a large marine engine

Large two-stroke marine engines are essential for global shipping, but their susceptibility to faults poses risks. Existing research on fault diagnosis and fault-tolerant control (FTC) for these engines lacks a comprehensive approach. This paper presents a holistic FTC strategy, systematically addressing sensor, actuator, and component faults. A model-based ship simulation is used for structural analysis and the design of fault diagnosis models. This not only identifies faults but quantifies their impact on engine operation. Additionally, optimal sensor placement is investigated to enhance fault diagnosis across the engine system. These diagnosis models are integrated into a supervisory FTC scheme, allowing reconfiguration in response to faults. Extensive simulation results demonstrate the effectiveness of this approach in maintaining safe engine speed control under various fault scenarios, successfully diagnosing and reconfiguring multiple sensor faults. This research provides a novel framework for improving the safety and reliability of large marine engines, addressing a critical need in the maritime industry.

Weak compound fault identification of gearboxes based on improved symplectic geometric mode decomposition and optimized cyclic kurtosis deconvolution

Abstract Given the complex and harsh operating conditions of gear transmission systems, gearboxes are prone to compound faults. These faults, involving multiple types, often couple together, causing the weak fault pulse characteristics to be entirely masked by strong environmental noise. This significantly complicates the extraction of relevant fault information from the gearbox. To address these issues, this paper proposes a method for extracting compound fault features based on improved symplectic geometric mode decomposition (SGMD) and optimized cyclic bispectrum deconvolution (CYCBD). Firstly, considering the periodic impact characteristics of different fault types, morphological envelope cyclic bispectrum is proposed to cluster the initial components obtained from SGMD decomposition, thereby adaptively separating different fault features contained in the compound fault signals. An adaptive filter length search strategy is subsequently introduced to optimize the CYCBD by deconvolving each initial fault component, thereby eliminating the interference caused by complex transmission paths and substantial environmental noise, which, in turn, enhances weak periodic fault pulses. Following this, the enhanced signals are subjected to envelope demodulation to extract fault characteristic frequencies, enabling the identification of various types of faults. The effectiveness and feasibility of the proposed method are demonstrated through both simulation signals and actual experimental data related to gearbox compound faults. Compared with existing methods, the proposed method demonstrates superior performance in identifying weak compound faults under strong environmental noise.

Fault-tolerant hierarchical energy management system for an electrical power system on more-electric aircraft

The concept of More-Electric Aircraft (MEA) has the potential to improve the environmental, economic, and reliability performance in the energy and transportation sectors. To achieve this potential, it has become a tendency to develop complex architectures of Electrical Power Systems (EPSs) for MEA to supply increasing electrical power demands. Moreover, a reliable and intelligent Energy Management System (EMS) is critical to coordinate the various EPS subsystems to ensure safe and efficient flight, following the real-time EPS operating requirements and safety criteria, while reducing the operating costs for all flight stages. This paper presents a fault-tolerant hierarchical EMS, for an innovative multi-converter-based aircraft EPS, to configure the system, ensure power distribution, and manage energy storage in multiple faulty scenarios over different time scales. There are two levels in this EMS: The High Level (HL) is based on Model Predictive Control (MPC), formulated by Mixed-Integer-Linear-Programming (MILP), to optimise the long-term EPS performance while considering future predictions; The Low Level (LL) adopts deterministic rules to cope with load changes and fault occurrences over the short term, during the HL sample intervals, with a faster clock. In particular, the LL controller contains four modes: to either cooperate with the HL online MPC or to operate independently, in either EPS normal or faulty conditions. The proposed EMS is evaluated in two cases, firstly considering load deviations in a normal operating scenario, and then considering behaviour in fault scenarios. The results indicate that the proposed EMS successfully reduces the EPS operational costs while ensuring quick responses to dynamic changes with either EPS component faults or EMS internal faults.

Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach

Bearings and gears are the pivotal components of mechanical systems and are prone to faults that can impact the system's overall performance. These components' condition monitoring and fault diagnosis are vital for maintaining system reliability and efficiency. In this research, a MEMS setup is initially developed, comprising a Raspberry Pi 4B+ CPU module, a NucleoF401RET6 MCU, an OLED screen, and an Adxl1002z accelerometer for acquiring vibration signals at the desired sampling frequency stored in the CPU memory. Further, an RF model is also developed to classify different types of faults based on features extracted from the acquired vibration data. The model evaluates the precision and reliability of the MEMS setup in capturing and classifying vibration signals. A detailed signal analysis is also conducted to determine the performance of the developed MEMS setup and to investigate the effect of bearing vibration signature due to gear fault and vice versa. The results indicate that bearing faults cause irregularities in the shaft's rotational speed, leading to modulation of the gear mesh frequency (gmf) of gears mounted on the affected shaft. Conversely, gear faults disrupt the shaft's rotational motion, imposing excessive loads on shaft-supported bearings. These disruptions result in distinct vibration patterns characterised by increased harmonics and side bands within the bearing frequency range. The RF model effectively identifies and classifies faults with high accuracy by leveraging its ability to prioritise the most significant vibrational features, resulting in improved predictive performance and robustness.

A fault hierarchical propagation reliability improvement method for CNC machine tools based on spatiotemporal factors coupling

Clarifying the fault propagation mechanism is one of the key methods for improving the machine tool's reliability. However, current modeling methods usually overlook the impact of spatiotemporal coupling factors on fault propagation, leading to a limited understanding of the fault propagation mechanism. Therefore, this paper proposes a fault hierarchical propagation reliability improvement method based on spatiotemporal factors coupling. Considering the coupling effects of component comprehensive importance, fault tolerance, and failure modes on the machine tool system, a spatiotemporal fault hierarchical propagation topological directed graph model was established. Based on this, an improved method for calculating fault propagation strength was proposed to identify weak links and critical fault propagation paths. The proposed method effectively addresses the critical path identification problem across CNC machine tool systems. Comparison results demonstrate that the proposed method can accurately identify critical fault propagation paths. Furthermore, the influence of various factors on these path sequences is studied in this paper. It extends the traditional modeling methods and theories to enhance the transparency of the fault propagation process within the machine tool system. This work provides theoretical support for maintenance decision-making.

Type-2 fuzzy adaptive robust fault-tolerant control for manipulators with disturbance observer

A configuration of interval type-2 (IT2) fuzzy adaptive fault-tolerant control strategy is designed for the position trajectory tracking of cooperated robot manipulators carrying a common object. First, a non-zero time-varying parameter is installed in IT2 FLS, then a property of universal approximation of IT2 fuzzy logic system (FLS) is proposed, where the non-zero time-varying parameter has the relation with the approximation precision of IT2 FLS. Second, for the unknown compounded disturbance, a disturbance observer is designed to estimate it and is integrated into the IT2 adaptive control. To improve the approximation accuracies, the novel IT2 FLSs with non-zero parameter are utilized to compensate for the uncertain nonlinear terms with including the uncertainties and fault components in the system, the fault-tolerant control with some adaptive laws are established using the stability theory of barrier Lyapunov function (BLF), where the approximation accuracies can be modified online using the provided adaptive laws with neglecting the number of fuzzy rules, the computation burden of the proposed control is greatly reduced because of a small number of updated laws. Finally, the simulation results are compared with those of several existing control methods, which confirms the validity of the designed control approach.

Utilization of Machine Learning and Explainable Artificial Intelligence (XAI) for Fault Prediction and Diagnosis in Wafer Transfer Robot

Faults in the wafer transfer robots (WTRs) used in semiconductor manufacturing processes can significantly affect productivity. This study defines high-risk components such as bearing motors, ball screws, timing belts, robot hands, and end effectors, and generates fault data for each component based on Fluke’s law. A stacking classifier was applied for fault prediction and severity classification, and logistic regression was used to identify fault components. Additionally, to analyze the frequency bands affecting each failed component and assess the severity of faults involving two mixed components, a hybrid explainable artificial intelligence (XAI) model combining Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) was employed to inform the user about the component causing the fault. This approach demonstrated a high prediction accuracy of 95%, and its integration into real-time monitoring systems is expected to reduce maintenance costs, decrease equipment downtime, and ultimately improve productivity.

Transient gas path fault diagnosis of aero-engine based on domain adaptive offline reinforcement learning

Real-time measurement parameters are crucial for diagnosing faults in aero-engine gas path performance, ensuring engine reliability, and mitigating potential economic losses. Traditional aero-engines performance diagnosis was mainly based on the measurements of steady-state condition and lacked the utilization of data under transient conditions. Gas path diagnosis of aero-engines under transient conditions is crucial for early fault detection and safety of flight within the envelope. The challenge lies in the inconsistent distribution of performance deviations caused by variable operating conditions, especially with complex fault types, which can undermine diagnostic credibility. To improve reliability of gas path diagnosis under transient conditions, an offline reinforcement learning fault diagnosis framework based on a transient aero-engine performance model is proposed. To address the issue of variable operating conditions during transient states, a domain adaptive approach is utilized to reconstruct the measurement baseline and facilitate the transfer of different performance deviation distributions. Additionally, by adding spool acceleration as a measurement parameter, the multi-component fault coupling is solved. Finally, validation with actual operating data simulates fault cases, demonstrating the proposed method's efficacy in quantitatively detecting gradual, sudden, and multiple component faults under transient conditions with high accuracy and efficiency. The method proposed in this study achieves a computational speed improvement by 64% compared to the conventional method, achieving a time of 0.13 seconds, with an average error of less than 0.00389%. Additionally, it demonstrates strong robustness in the presence of noise, with an average error of less than 0.03125%. This proposed method improves real-time fault detection under transient conditions for its higher accuracy and efficiency, and therefore significantly enhance gas path health monitoring and diagnosis capability.

Vibration source inversion-based fault diagnosis: Approach and application

The gear transmission system of industrial equipment is characterized by a highly integrated structure, non-stationary operating conditions, and large loads. Therefore, the attenuation of fault signal characteristics and the complexity of operating conditions in gear transmission systems significantly affect the effectiveness of vibration signal-based fault diagnosis. This paper proposes a unified diagnostic framework based on vibration source inversion for two typical transmission component fault signal phenomena: meshing frequency modulation sidebands and equally spaced fault characteristic frequency interval harmonic clusters. This approach incorporates the vibration transfer path analysis of passive subsystems and identifies bearing dynamic forces, followed by their decomposition, reconstruction, and sensitive frequency band localization. It enables accurate diagnosis of complex gear transmission systems under non-stationary conditions with limited vibration testing. The effectiveness and advantages of this method over existing mainstream signal processing techniques are validated through actual cases of gear and weak bearing fault diagnosis on a complex planetary gear transmission system test bench.

Research on noise reduction method of centrifugal pump coupling fault signal based on LMD-FE-IWT

Abstract Aiming at the problem that the coupling fault signal of centrifugal pump presents the complexity of mutual coupling due to the mutual influence of various fault components, and it is easy to be affected by the surrounding environmental noise, which leads to the difficulty of noise reduction, the LMD-FE-IWT noise reduction method is proposed. This method first utilizes local mean decomposition (LMD) to adaptively decompose the vibration signal, and calculates the fuzzy entropy of each component. According to fuzzy entropy, the components are divided into two categories: disordered and ordered. After the improved wavelet threshold denoising of the disordered component, it is reconstructed with the ordered component to achieve the denoising effect. By analying the simulation signal and experimental data, the results show that, the proposed method outperforms other methods, with improvements in signal-to-noise ratio by 55.95%, 48.86%, and 31.71% for the simulation signal noise reduction. The effect is significant, enabling the effective extraction of characteristic frequencies associated with different faults for the experimental signal noise reduction.