Faults In Complex Systems Research Articles

Complex system fault diagnosis based on interpretive structural model

To realize the fault diagnosis of complex systems, a fault diagnosis method for complex systems based on the explanatory structural model is proposed. According to the fault mechanism analysis, the fault association matrix of system elements is established. The explanatory structural model is applied to transform the fault association relationship of complex systems into an intuitive hierarchical structural model through matrix transformation, so as to realize the structuring and hierarchical propagation of system faults. The PageRank algorithm is introduced to evaluate the influence and impact of system element faults. According to the influence and impact of the same-layer components and the fault transmission logic, the main cause of system fault transmission is clarified, which provides a basis for fault diagnosis. Finally, a specific application is carried out with a certain device as an example to verify the effectiveness of the method.

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic mode decomposition with control is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k ) is input into the k-nearest neighbor classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and Vk (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k , and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

GRAND: GAN-based software runtime anomaly detection method using trace information

Software runtime anomaly detection can detect manifestations (known as anomalies) caused by faults in complex systems before they lead to failure. Whereas most existing methods use external performance indicators, this study uses internal execution traces to reveal failures not only related to software performance issues but also functional errors. A neural network model called GRAND, which combines a variational autoencoder and a generative adversarial network, is proposed to mine anomalies in the execution trace. Cassandra, a widely used database system, was used as a representation to conduct the empirical study. The dataset was collected under a well-designed operational profile that contained 5180 time series, each containing more than ten million data points. GRAND achieved a higher detection performance than the other two SOTA baseline models, with a 99% F1-score compared with 93% and 87%. Ablation studies show that the workload information used in GRAND can determine whether the current internal status is consistent with the task, thus achieving a 16% improvement in the F1-score. The attention mechanism used for data fusion can achieve a 32% improvement in the F1-score.

Joint feedback control and fault diagnosis disambiguation

This paper proposes a model-based diagnosis approach to detect and isolate intermittent faults in complex systems that operate under feedback control. The feedback control attempts to compensate for model uncertainties and deviations from nominal behavior, but these uncertainties are crucial for accurate fault diagnosis. We focus on faults that are observable only in a particular region of the state space, which is rarely reached in nominal behavior. To address this, we present an approach that considers both control requirements and diagnosis uncertainty in an optimization problem similar to model-predictive control. We compute perturbations on control signals that forces the system to reach states where faults are detectable. We apply our approach to a quadrotor system under motion feedback control, demonstrating the effectiveness of our method. Our approach has the potential to improve the resilience of complex systems by quickly detecting and recovering from disruptive events.

A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods

Fault prognosis is an important area of research that aims to predict and diagnose faults in complex systems. The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equipment components in optimal condition in order to avoid downtime that may cause significant disruption. Due to this reason, in recent years, there has been an increasing interest in creating innovative methods for fault prognosis that can increase system reliability and reduce maintenance costs. Gear pumps are widely utilised in a variety of industrial applications, and their reliability and effectiveness are crucial for achieving optimal system performance. Gear pumps, on the other hand, are prone to malfunctions and failures, which can result in substantial downtime and maintenance costs. The challenge is to develop a fault prognosis approach that is reliable and accurate enough to detect and diagnose defects in a gear pump before they cause system failures. This paper proposes a novel computational strategy for the fault prognosis of an external gear pump using Machine Learning (ML) approaches. Due to the unavailability of sufficient experimental data in the vicinity of failure mechanisms, a novel approach to generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic (CFD) model of the gear pump in healthy and faulty working conditions is presented. However, considering the computational demand for rerunning the same CFD simulations, novel synthetic data generation techniques are implemented by perturbing the frequency content of the time series to recreate other working conditions and constructing degradation behaviour using linear and cubic interpolation methods for run-to-failure scenarios. The synthetically created datasets are used to train the underlying ML metamodel for fault prognosis. Two types of ML algorithms are employed for fault prognosis: Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms. A series of numerical examples are shown, allowing us to infer that the proposed modelling technique is feasible in an industrial setting and that employing the MLP algorithm delivers superior fault prognosis results when compared to SVM. Furthermore, datasets generated using the cubic interpolation method have lower prediction errors than datasets generated using the linear interpolation method due to the smoother degradation behaviour in the data.

A new complex system fault detection method based on belief rule base for unreliable interval values

Failures to equipment such as milling machines and inertial navigation systems (INSs) can affect their normal operation, resulting in economic losses and personal injury in severe cases. Therefore, fault detection is of great importance. Belief rule base (BRB) is an expert system that plays an important role in fault detection. The traditional BRB has some problems in the explosion of the number of combination rules, the process of model inference, and the process of parameter optimization. To better deal with the above problems, this paper proposes a complex system fault detection method based on an interval-valued BRB fault detection interval-valued (FDIV) and provides the construction and inference process of the method. In the method construction, the form of interval value and disjunction rules are introduced to solve the problem of the number explosion of combination rules, the indicator reliability is added to improve the accuracy of the method, and a new calculation method of rule availability is proposed. In the inference process, twice fusions are made based on evidence reasoning (ER) analysis algorithm and ER rule algorithm respectively to deal with the interval uncertainties. Moreover, the proposed FDIV method is optimized by the projection covariance matrix adaptive evolutionary strategy algorithm projection covariance matrix adaptive evolutionary strategy (P-CMA-ES). Finally, the effectiveness of the proposed method was verified through the research on milling fault detection and the experimental verification of INS fault detection. The superiority of the model was also confirmed through comparative experiments.

Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model

Instability at transient frequency caused by faults in complex power systems is one of the greatest threats to operational safety. By analyzing the frequency response of power system in real-time and adopting control strategies promptly, power system accidents can be efficiently prevented. While existing online analysis methods integrate physical-driven and data-driven methodologies, they do not effectively utilize frequency timing characteristics. Consequently, a swarm intelligence fusion model, which integrates physical-driven and data-driven methods, is proposed as an improved frequency response analysis method. The transient frequency affecting components are separated into primary state variables and system time series data based on the properties of the time sequence. To preserve the actual relationship of the electrical mechanism model, the system frequency response (SFR) model is used as the physical-driven method for the primary state variables of the system. The Long Short Term Memory (LSTM) network was used as the data-driven method to extract timing features and correct the SFR model’s prediction using the system time series data as input. The two methods are combined using the bootstrap mode to form the fusion model, and the structure of the model is optimized using an improved sparrow search algorithm (ISSA), a swarm intelligence optimization algorithm. The model structure is adapted autonomously, implementing a method for online frequency response analysis. The simulation on the New England 39-bus system has verified that the method can quickly and accurately calculate the dynamic process of frequency response after a large-scale disturbance.

Investigation of Vibration Signal Processing using Short Time Fourier Transform for Internal Combustion Engine

The methods of short-time Fourier transform (STFT) signal processing have been used to enhance the condition monitoring of machines. It can be better to use in reciprocating machines such as internal combustion engines, as the signal is non-stationary. The FFT usage wouldn’t be beneficial in this case as it treats stationary signals such as those captured from rotating machines. The vibration of machines became challenging, as for the engine, pack up, faults in such complex systems can lead to frequency alternation and amplitude. In the present study, the vibration signal was acquired from the engine at the vicinity of the piston 2 and 3 to get acceptable signal intensity. The normal state is measured at idle speed condition and two other speeds 2000 and 3000 rpm. Three faults are investigated namely; misfire of one cylinder, two cylinder misfire and ignition timing. Then, the signal processed using STFT was used to distinguish and detect fault using vibration signal. The output signals were exported to MATLAB for further analysis. The experimental results revealed that the normal condition had more vibration than one cylinder misfire than two cylinder misfire due to the damping effect. In addition, the vibration levels at ignition timing (3 BTDC), (3 ATDC), (6 BTDC) and (6 ATDC) were recorded as maximum when compared to normal condition at 5 BTDC.

Analysis of Faults in Software Systems Using Tsallis Distribution: A Unified Approach

The identification of the appropriate distribution of faults is important for ensuring the reliability of a software system and its maintenance. It has been observed that different distributions explain faults in different types of software. Faults in large and complex software systems are best represented by Pareto distribution, whereas Weibull distribution fits enterprise software well. An analysis of faults in open-source software endorses generalized Pareto distribution. This paper presents a model, called the Tsallis distribution, derived using the maximum-entropy principle, which explains faults in many diverse software systems. The effectiveness of Tsallis distribution is ascertained by carrying out experiments on many real data sets from enterprise and open-source software systems. It is found that Tsallis distribution describes software faults better and more precisely than Weibull and generalized Pareto distributions, in both cases. The applications of the Tsallis distribution in (i) software fault-prediction using the Bayesian inference method, and (ii) the Goal and Okumoto software-reliability model, are discussed.

Neuro-fuzzy algorithm for clustering multidimensional objects in conditions of incomplete data

The paper is considered development of fuzzy expert system model for identifying faults in complex systems using data mining methods based on searching for hidden patterns in databases. The use of neural network technologies makes it possible to detect nonlinear dependencies of input and output data, improve the quality of an objective assessment of the state of complex technical objects, which ultimately will reduce the number of emergency situations during operation. A method is proposed for identifying the optimal number of fuzzy clusters in the space of training examples and determining, on their basis, the parameters of the membership functions for the input variables and inference results. Considered a neuro-fuzzy algorithm for clustering multidimensional objects in conditions of incompleteness and fuzzy initial information.

A Novel Method for Predicting Fault Labels of Roller Bearing by Generalized Laplacian Matrix

Because mechanical failures are accompanied by contingency and randomness, fault data is often difficult to obtain, and fault labels are also difficult to assign. The lack of data and fault labels have become important issues that restrict the development of fault diagnosis. The paper proposed a generalized Laplacian label prediction (GLLP) algorithm, which mainly uses the generalized Laplacian matrix and calculated a new locally smooth term. Therefore, data points with ambiguous and unclear labels will be assigned a small label value, while samples with more certain labels can get a more confident label value. The effectiveness of the method is verified on the public dataset and the real test rig dataset, and it is expected that this method can be extended to more complex mechanical system fault diagnosis.

A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems

Fault diagnosis is a key way to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a fault diagnosis method based on belief rule base with mixed reliability (BRB-mr). Different from the traditional BRB, this method considers two kinds of interference factors that affect the observation data in engineering practice, including the performance of sensors and the influence of external environment, and we quantify them as static reliability and dynamic reliability of attributes in BRB. In order to integrate two kinds of reliability factors into the reasoning of BRB, a discount method is developed based on Dempster-Shafer theory (D-S theory), which is helpful for more accurate diagnosis. In this paper, the effectiveness and practicability of the method are verified by a single fault of the running gear, and the supplementary numerical data verified its feasibility in multiple fault mode diagnosis. Then this method is compared with traditional methods. The result shows BRB-mr model has stronger diagnostic ability to identify faults and it has a certain engineering application value to be extended to other complex system fault diagnosis.

Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra

Fault diagnosis plays a vital role in prognostics and health management. Researchers have devoted their efforts in enhancing the accuracy of fault diagnosis. However, diagnosis of compound faults in complex systems is still a challenging task. The problem lies in the coupling of multiple signals, which may conceal the characteristics of compound faults. Taking a rolling bearing as an example, this study aims to boost the accuracy of compound fault diagnosis through a novel feature extraction approach to making the fault characteristics more discriminative. The approach proposes an adaptive dual-tree complex wavelet packet transform (DTCWPT) with higher order spectra analysis. To flexibly and best match the characteristics of the measured vibration signals under analysis, DTCWPT is first adaptively determined by the minimum singular value decomposition entropy. Then, higher order spectra analysis is performed on the decomposed frequency sensitive band for feature extraction and enhancement. The proposed approach is used to analyze experimental signals of a bearing’s compound faults and found effective.

A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis

A novel fault diagnosis method is proposed for rolling bearing by combining extreme-point symmetric mode decomposition (ESMD) composite multiscale weighted permutation entropy (CMWPE) and gravitational search algorithm based on multiple adaptive constraint strategy (MACGSA) optimized least squares support vector machine (LSSVM). In order to solve the problem of intrinsic mode function (IMF) modal aliasing and small differences in fault features, ESMD and CMWPE are used to obtain a more sensitive high-dimensional feature vector set. Aiming at the low accuracy of LSSVM fault diagnosis, MACGSA was used to optimize LSSVM to improve the accuracy of fault diagnosis. ESMD is used to process the rolling bearing data to obtain a series of IMFs; Then, extracting the CMWPE values of IMFs to form a high-dimensional feature vector set; Finally, the MACGSA-LSSVM model is adopted to achieve fault classification. Compared with other diagnostic methods, this method has higher diagnostic accuracy.

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis.

To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.

A novel method of combining nonlinear frequency spectrum and deep learning for complex system fault diagnosis

A novel fault diagnosis method is proposed for complex systems by combining nonlinear frequency spectrum and stacked denoising auto-encoders (SDAE). In order to solve the problem of large calculation amount of generalized frequency response functions (GFRF), one-dimensional nonlinear output frequency response functions (NOFRF) are used to obtain nonlinear frequency spectrum. In order to overcome the problem of weak ability of fault features extraction, stacked denoising auto-encoders (SDAE) neural network is adopted to extract the fault features from nonlinear frequency spectrum. In this novel method, four orders nonlinear frequency spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) are obtained by identification algorithm; Then, choosing suitable sampling points from four orders frequency spectrum to construct high-dimensional data; Finally, stacked denoising auto-encoders (SDAE) neural network is designed to realize the output of fault classification. Simulations indicate that the proposed method has good real-time performance and high diagnosis accuracy.

Arrays for combinatorial interaction testing: a review on constructive approaches

Interaction faults in component-based complex systems have been investigated from various aspects in the past few decades. The key issue is that, in a complex system, not only a single variable may cause the failure, but also the interactions among different variables. Combinatorial interaction testing has been established as one of the most fundamental and effective tools for identifying and locating the faulty interactions. This paper presents a review of the brief history, essential problems, and significant results on arrays for combinatorial interaction testing, including covering arrays, locating arrays, detecting arrays, and their variations. Specific approaches on explicit constructions for covering arrays involving information-theoretic methods are reviewed. Finally, we propose two improved bounds for studying the optimality of locating arrays.

A Self-Learning Fault Diagnosis Strategy Based on Multi-Model Fusion

This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%.

Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems

As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault‐tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model‐building process. However, the implementation of FRSN P systems is still at a manual process, which is a time‐consuming and hard labor work, especially impossible to perform on large‐scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large‐scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.

Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System

Fault detection and diagnosis in the chemical industry is a challenging task due to the large number of measured variables and complex interactions among them. To solve this problem, a new fault diagnosis method named Fisher discriminative sparse representation (FDSR), based on deep belief network (DBN), is proposed in this paper. We used DBN to extract the features of all faulty and normal modes. Features extracted by the DBN were used to learn subdictionaries, then the overcomplete dictionary was constructed by cascading all subdictionaries in order, and each dictionary atom corresponded to class labels. The Fisher discrimination criterion (FDC) was applied to the dictionary learning to ensure smaller within-class scatter but greater between-class scatter. The quadratic programming method was applied to estimate the sparse coefficients simultaneously class by class. Therefore, both the reconstruction error and sparse coefficients were discriminative, so that the reconstruction error after sparse coding can be used for pattern classification. An experiment performed on the Tennessee Eastman (TE) process indicated that compared with the traditional monitoring methods, the FDSR based on DBN produced more interpretable results and achieved superior performance for feature extraction and classification in the field of complex system fault diagnosis. T-distributed stochastic neighbor embedding (t-SNE) appropriately visualized the performance of the proposed method and produced reliable fault diagnosis results.