Detectable Faults Research Articles

Model-based diversity-driven learn-to-rank test case prioritization

Model-based Test Case Prioritization utilizing similarity metrics has proved effective in software testing. However, the utility of similarity metrics in it varies with test scenarios, hindering its universal effectiveness and performance optimization. To tackle this problem, we propose a Diversity-driven Learn-to-rank model-based TCP approach, named DLTCP, for optimizing early fault detection performance. Our method first employs the whale optimization algorithm to search for a suitable set of similarity metrics from a pool of existing candidates. This search process determines which metrics should be used. According to each selected metric, test cases are then prioritized. The resulting test case rankings are used as the training data for DLTCP. Finally, the proposed method incorporates random forest to train a ranking model for test case prioritization. As such, it can fuse multiple similarity metrics to improve the TCP performance. We conduct extensive experiments to evaluate our method’s performance using the average percentage fault detected (APFD) as metric. The experimental results show that DLTCP achieve an average APFD value of 0.953 for seven classic benchmark models, which is 11.37% higher than that of the state-of-the-art algorithms. It can well select a set of similarity metrics for effective fusion, demonstrating competitive performance in early fault detection.

Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.

This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).

Exploiting DBSCAN and Combination Strategy to Prioritize the Test Suite in Regression Testing

Test case prioritization techniques improve the fault detection rate by adjusting the execution sequence of test cases. For static black-box test case prioritization techniques, existing methods generally improve the fault detection rate by increasing the early diversity of execution sequences based on string distance differences. However, such methods have a high time overhead and are less stable. This paper proposes a novel test case prioritization method (DC-TCP) based on density-based spatial clustering of applications with noise (DBSCAN) and combination policies. By introducing a combination strategy to model the inputs to generate a mapping model, the test inputs are mapped to consistent types to improve generality. The DBSCAN method is then used to refine the classification of test cases further, and finally, the Firefly search strategy is introduced to improve the effectiveness of sequence merging. Extensive experimental results demonstrate that the proposed DC-TCP method outperforms other methods in terms of the average percentage of faults detected and exhibits advantages in terms of time efficiency when compared to several existing static black-box sorting methods.

A comparative investigation between rule- and inverse model-based fault detection and diagnostics for HVAC control systems

Fault detection and diagnostics (FDD) tools provide valuable information regarding system faults and deviation from expected operation. Most existing FDD tools apply rule-based fault detection algorithms that generate an alarm when a rule is met; however, these tools cannot evaluate the overall performance of a system. Inverse-model-based FDD algorithms can be deployed to complement the fault alarms triggered by rule-based building energy management systems (BEMS). This paper examines the faults detected by rule- and inverse model-based algorithms used to detect faults in multiple zone variable air volume air handling unit systems. The capability of the rule- and inverse model-based algorithms in detecting and diagnosing faults is demonstrated through illustrative examples using data from three commercial buildings in New Brunswick, Canada. The results show that inverse model-based algorithms could diagnose faults that were not detected by the rule-based FDD algorithms implemented in a commercially available BEMS tool.

Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application

Prioritizing test cases based on several parameters where important ones are executed first is known as test case prioritization (TCP). Code coverage, functionality, and features are all possible factors of TCP for detecting bugs in software as early as possible. This research was carried out to test and compare the effectiveness Swarm Intelligence algorithms, where Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO) algorithms were implemented to find the fault detected and execution time as these are the curial aspects in software testing to ensure good quality products are produced within the timeline. As web applications are commonly used by a board population, this research was carried out on an Online Shopping application represented as Case Study One and Education Administrative application known as Case Study Two. In recent years, TCP has been implemented widely, but none has implemented on web application which was conducted to fill the gaps and produce a new contribution in this area. The outcome was compared using Average Percentage Fault Detected (APFD) and execution time. For Case Study One, the APFD value was 0.80 and 0.71 while the execution time was 8.64 seconds and 0.69 seconds respectively for ABC and ACO. For Case Study Two, the APFD values were 0.81 and 0.64 while the execution time was 8.83 seconds and 1.22 seconds for ABC and ACO. It was seen that both algorithms performed well in their respective ways. ABC had shown to give a higher value for APFD while ACO had converged faster for execution time.

Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant

Detecting and monitoring faults in solar photovoltaic (PV) systems is crucial to ensure optimal efficiency and prevent safety and fire hazards. However, the conventional operation and maintenance (O&M) of solar PV systems do not utilize machine learning for fault detection and classification. This poses challenges for plant operators, especially those managing large-scale solar (LSS) PV plants, who typically rely on manual approaches to screen large amounts of electrical data and inspect numerous string panels. Consequently, the cost of O&M is high. This study aims to use advanced machine-learning techniques to detect anomalies in a Large-Scale Photovoltaic (LSSPV) plant. The study collects data from the plant which located in the central of Peninsular Malaysia and employs K-Means for clustering and Long-Short Term Memory (LSTM) for anomaly detection in the predicted electrical current of string modules. The model is developed using Jupyter Notebook from the Python Package Index. To validate the accuracy of the proposed model, the study compares LSTM with Artificial Neural Network (ANN) using relative error as the evaluation metric. The results indicate that the LSTM method identifies anomalies in the predicted output current of the string modules more accurately and with lower relative error than the conventional ANN technique. The proposed model could help plant operators perform predictive maintenance of LSSPV plants at a minimal cost and time.

Structural models of Mealy finite state machines detecting faults in control systems

The subject matter of this article is a control system for unmanned aerial vehicles (UAVs) whose mathematical model is a finite state machine (FSM). The goal is to develop FSM structural models that enable (1) detection of multiple faults of FSM elements caused by an electromagnetic pulse or laser beam, and (2) prevent negative impacts on the controlled object. The tasks to be solved are as follows: to develop FSM structural models to detect invalid input vector X for the whole FSM and in each state, to detect invalid output vector Y for the whole FSM, at each transition and in each state, invalid code of the present (current) state, invalid code of the next state, and invalid transition between states; to determine the possible causes of the faults, which can be the failure in the logic Φ of forming the code of the next state, the invalid input vector X, the failure in the feedback circuit, the failure in the logic Ψ of forming the output vector, the failure in the state register R, the failure in the wire between the FSM input and the input of the logic Ψ; development of a combined structural model for the detection of all listed faults with a minimum number of additional combinational circuits, as well as a structural model that combines all additional combinational circuits. The methods used are: the theory of finite state machines, structural models of FSMs, state encoding methods of FSMs, representation methods of FSMs, and Verilog hardware description language. The following results were obtained: (1) the Mealy FSM structural models were developed to detect all the above mentioned faults, (2) the combined FSM structural models were developed, and (3) the possible causes of faults detected by each FSM structural model were identified. Experimental studies have shown that for the presented FSM structural models, the area overhead averages 3-23%, for one-hot encoding of FSM states, and 2-8%, for binary encoding of FSM states. Conclusions. The scientific novelty of the obtained results consists in the following for the first time FSM faults that are not caused by radiation and cosmic rays but by an electromagnetic pulse affecting the control device are considered; the number of faults is not limited for the state codes as well as for the input and output vectors; the faults can be detected not only in the state register R but also in the input vector X, in the logic Φ of generating the next state code, in the logic Ψ of generating the output signals, and in the feedback circuit; the invalid transitions of FSMs and the transitions to invalid states are also detected; the proposed structural models not only detect FSM failures but also prevent their negative impact on the controlled object; combined structural models allow simultaneous detection of faults in all elements of the FSM. Future research will focus on developing structural models for correcting FSM failures.

Semantic‐aware two‐phase test case prioritization for continuous integration

SummaryContinuous integration (CI) is a widely applied development practice to allow frequent integration of software changes, detecting early faults. However, extremely frequent builds consume amounts of time and resources in such a scenario. It is quite challenging for existing test case prioritization (TCP) to address this issue due to the time‐consuming information collection (e.g. test coverage) or inaccurately modelling code semantics to result in the unsatisfied prioritization. In this paper, we propose a semantic‐aware two‐phase TCP framework, named SatTCP, which combines the coarse‐grained filtering and fine‐grained prioritization to perform the precise TCP with low time costs for CI. It consists of three parts: (1) code representation, parsing the programme changes and test cases to obtain the code change and test case representations; (2) coarse‐grained filtering, conducting the preliminary ranking and filtering of test cases based on information retrieval; and (3) fine‐grained prioritization, training a pretrained Siamese language model based on the filtered test set to further sort the test cases via semantic similarity. We evaluate SatTCP on a large‐scale, real‐world dataset with cross‐project validation from fault detection efficiency and time costs and compare it with five baselines. The results show that SatTCP outperforms all baselines by 6.3%–45.6% for mean average percentage of fault detected per cost (APFDc), representing an obvious upward trend as the project scale increases. Meanwhile, SatTCP can reduce the real CI testing by 71.4%, outperforming the best baseline by 17.2% for time costs on average. Furthermore, we discuss the impact of different configurations, flaky tests and hybrid techniques on the performance of SatTCP, respectively.

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. This paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

Confidence set-based analysis of minimal detectable fault under hybrid Gaussian and bounded uncertainties

This paper proposes a confidence set-based computational method of minimal detectable fault (MDF) based on the confidence set-separation condition between the healthy and faulty residual sets for discrete linear time-invariant systems. The state-estimation-error dynamics for the analysis of MDF under hybrid random and bounded uncertainties is divided into two sub-dynamics. The first sub-dynamics is only affected by the bounded uncertainties. Under the precondition of Schur stability, an outer-approximation of minimal robust positively invariant set with any given precision is obtained. While the second sub-dynamics is only affected by the random uncertainties following the Gaussian distributions. It is proved that the behavior of the second sub-dynamics at steady stage also follows a certain Gaussian distribution, which can be bounded by confidence zonotopes given a proper confidence level. MDF for actuator and sensor faults can be obtained by solving a non-convex optimization problem to minimize the magnitude of fault subjected to the residual set-separation constraints, which is equivalent to compute a distance from the origin to hyperplane along a fixed direction by exploiting the geometric property. At the end of this paper, a two-link manipulator model and a vehicle model are used to verify the effectiveness of our proposed method.

Image registration based fault localization in panoramas of mountain-mounted PV plants

Large-scale photovoltaic (PV) plants have been widely distributed in the mountains. However, the complex geographical environment and the irregular arrangement of PV strings bring great challenges to maintenance. In this paper, a novel scheme combining an improved YOLOv5s model and image registration algorithms is proposed to detect and locate module faults in mountain-mounted PV plants (MPVs) via aerial dual-light videos and panoramas. The improved YOLOv5s used to detect faults in IR images becomes more lightweight owing to the integration of MobileNetV3 and ECA-Net. To realize fault localization in panoramas of MPVs, image registration algorithms are adopted. IR and visual images are simultaneously extracted frame by frame from dual-light videos. Faults detected in the IR image are first mapped to the corresponding visual image by the homography (H1) matrix. To map faults from the visual image to the panorama, another registration between the visual image and panorama is achieved. Multi-scale matching between the visual image and the panorama is first performed to reduce the area of registration. The conversion of grayscale probability density function (GPDF) of the visual image and the adoption of AKAZE feature detection algorithm make the homography (H2) matrix used for registration more accurate. A MPV with a power generation of 212.85 kW is used for testing. The precision and recall of fault detection are 88.31% and 91.67%, respectively. The accuracy of locating faults to the corresponding modules is 85.3%, and the recall of locating is 94.12%. Experimental results show that the proposed scheme can effectively identify faults and accurately locate faults in MPVs.

Memory-Event-Triggered Fault Detection of Networked IT2 T-S Fuzzy Systems.

In this article, a networked fault detection (FD) problem is investigated for interval type-2 T-S fuzzy systems. A novel adaptive memory-event-triggered mechanism (METM) is proposed by introducing historical information of the measured output in a prescribed sliding window. The current measured output in the traditional event-triggered mechanism is replaced by a weighting function-based historical information. As a result, the data releasing rate can be effectively reduced and maltriggering events aroused by unknown abrupt disturbance or measurement noise can be avoided as well. Meanwhile, an adaptive threshold depending on the historical information is utilized to further adjust the data releasing rate. The FD filter is designed and derived in terms of linear matrix inequalities to guarantee the H∞ performance of fault detected systems. Finally, a hardware-in-loop simulation experiment platform is built to manifest the effectiveness of the proposed METM-based FD method.

Minimal Detectable and Isolable Faults of Active Fault Diagnosis

This article aims to compute guaranteed minimal detectable and isolable faults of set-based active fault diagnosis methods for discrete linear time-invariant systems. First, guaranteed detectable and isolable faults of set-based active fault diagnosis methods are defined in this article. Second, an augmented vector is defined to integrate the effect of both inputs and faults such that the couplings of inputs and faults can be handled effectively. Third, guaranteed minimal detectable and isolable faults are computed by solving a mixed integer quadratic fractional programming problem under a proposed theoretic framework. At the end of this article, an example is used to illustrate the effectiveness of the proposed solution.

Path Unselection for Path Delay Fault Test Generation

Path selection procedures identify path delay faults whose tests detect small delay defects. Path selection criteria are positive in the sense that they point to paths that should be selected, e.g., the longest paths. However, the longest paths are in many cases untestable, and the number of paths can be large when the longest testable paths are considered. For this scenario, it is advantageous to have a negative path selection procedure that excludes paths from consideration as targets for test generation. This provides the flexibility to identify detectable path delay faults among a reduced number of path delay faults. Such a procedure is developed in this article and referred to as a path unselection procedure. The procedure performs linear-time traversals of the circuit to unselect fan-out branches, and thus exclude from consideration paths that are shorter than the ones it keeps. It also uses available information about detectable and undetectable path delay faults to unselect fan-out branches or avoid unselecting them. To demonstrate its use, the procedure is applied to benchmark circuits as part of an iterative test generation procedure whose goal is to target manageable numbers of the longest path delay faults.

Estimating energy savings from HVAC controls fault correction through inverse greybox model-based virtual metering

Fault impact analysis is an important phase of fault detection and diagnosis (FDD), enabling prioritization of the faults detected and allocation of resources to procure services and materials. However, many existing fault impact analysis workflows rely on building performance simulation (BPS) models with high engineering cost, limiting their use in FDD software. To this end, this paper introduces an inverse greybox model-based fault impact analysis method using building automation system (BAS) trend data from variable air volume (VAV) air handling unit (AHU) systems to quantify the heating and cooling energy impact of control system faults. It trains inverse models of a VAV AHU system by employing a sequential parameter estimation algorithm, formulates virtual meters quantifying the sensible heat transfer rate from AHU heating and cooling coils and the perimeter heating devices, and employs the virtual meters to estimate heating and cooling energy use of an existing VAV AHU system and the same system after fault correction. The method was used with a synthetic BAS dataset generated by using an EnergyPlus model of a 26-zone VAV AHU system in Ottawa, Canada. The results indicate that the virtual meters could estimate the monthly energy use for heating and cooling at a coefficient of variation of the root mean squared error (CV(RMSE)) of 4–6%. The method was demonstrated with measured BAS data from a 72-zone VAV AHU system, revealing an energy savings potential of 43% for heating and 22% for cooling through fault correction.

Zonotopic recursive least‐squares parameter estimation: Application to fault detection

SummaryThe uncertain parameters estimation problem for linear discrete‐time systems modeled in regression form and assuming an unknown but bounded description of the noise, is the main subject of this work. Particularly, zonotopic sets for bounding the parametric uncertainty are considered. Then, a zonotopic recursive least‐squares (ZRLS) estimator is proposed and compared with its stochastic counterpart (RLS), as well as with the set‐membership (SM) approach. Likewise, the parameter estimation problem is addressed from the Bayesian general framework to define the relationship between stochastic and deterministic approaches. Moreover, both ZRLS and SM applications applied to fault detection are assessed taking as a reference the minimum detectable fault in the worst case. Finally, a well‐known quadruple‐tank process is used to illustrate the estimation and fault detection results.

Fault detective: Automatic fault-detection for solar thermal systems based on artificial intelligence

Fault-Detection (FD) is essential to ensure the performance of solar thermal systems. However, manually analyzing the system can be time-consuming, error-prone, and requires extensive domain knowledge. On the other hand, existing FD algorithms are often too complicated to set up, limited to specific system layouts, or have only limited fault coverage. Hence, a new FD algorithm called Fault-Detective is presented in this paper, which is purely data-driven and can be applied to a wide range of system layouts with minimal configuration effort. It automatically identifies correlated sensors and models their behavior using Random-Forest-Regression. Faults are then detected by comparing predicted and measured values.The algorithm is tested using data from three large-scale solar thermal systems to evaluate its applicability and performance. The results are compared to manual fault detection performed by a domain expert. The evaluation shows that Fault-Detective can successfully identify correlated sensors and model their behavior well, resulting in coefficient-of-determination scores between R²=0.91 and R²=1.00. In addition, all faults detected by the domain experts were correctly spotted by Fault-Detective. The algorithm even identified some faults that the experts missed. However, the use of Fault-Detective is limited by the low precision score of 30% when monitoring temperature sensors. The reason for this is a high number of false alarms raised due to anomalies (e.g., consecutive days with bad weather) instead of faults. Nevertheless, the algorithm shows promising results for monitoring the thermal power of the systems, with an average precision score of 91%.

A New Method to Compute Minimal Detectable and Isolable Faults of Active Fault Diagnosis*

This paper proposes a new method for the computation of guaranteed minimal detectable and isolable faults of set-based active fault diagnosis for multiplicative actuator faults. Compared with our previous work, the proposed method in this paper has the important advantage of low computational complexity. In particular, by handling the coupling of faults and inputs and using an objective function describing the minimal detectable and isolable faults, a new two-step optimization method is proposed to analyze and compute the minimal detectable and isolable faults. At the end of this paper, two examples are used to show the effectiveness of the proposed method and the advantage of low computational complexity.

A Zonotopic Velocity Sensor Fault Detection Approach for Autonomous Underwater Vehicle

This paper studies the problem of velocity sensor fault detection for Autonomous Underwater Vehicles. A novel fault detection method is proposed based on zonotopes. The fault is detected based on the check of the intersection between the prediction state set and the measurement state set. The performance of the proposed method is evaluated via a minimum detectable fault set. Numerical simulation is conducted to demonstrate the validity and viability of the proposed method.

Fault Tolerant Load Balancing with Quadruple Osmotic Hybrid Classifier and Whale Optimization for Cloud Computing

Cloud Computing (CC) environment is developing as a recently discovered caliber for computing applications over the network. Fault tolerance is one of the foremost issues in CC environment. Since the negligence in resource have a profound effect on job execution, throughput, response time and performance of the entire network. In this work, in order to address the issue, Quadruple Osmotic Hybrid Classification and Whale Optimization (QOHC-WO) is introduced to fault-tolerance under the requirement of different user request tasks. Initially, Quadruple Fault Tolerance Level is applied to allocate the fault tolerance level. Followed by, Hybrid Vector Classifier is used to categorize the user request tasks (task) and cloud server nodes (node). Then, the Osmotic function is employed for performing the migration among virtual machines with lesser response time. This helps to solve the maximum level of fault issue. Finally, Improved Whale Optimization Algorithm is applied to find the optimal allocation of tasks with the corresponding node. In addition, the Bandit function and Whale optimization are used to address the trade-off between exploitation and exploration. Experimental setup of the proposed QOHC-WO method and existing methods are carried out with different factors such as task response time, the number of VM migrations, and percentage of fault detected rate with respect to a number of tasks. The analyzed results validate that the proposed QOHC-WO method achieves a higher fault detection rate with minimum response time as well as task migration than the state-of-the-art methods.