Fault Detection Capability Research Articles

MTR Model-Based Testing Framework MTR Model-Based Testing Framework MTR Model-Based Testing Framework

In this article we propose a novel, free and open- source model-based testing framework for finite state machine specifications. The various model handling and test generation options make the framework suitable for testing complex systems and provide a solid background for investigating different automated test design methodologies. The complexity and fault detection capabilities of the available algorithms are investigated through analytical analyses and simulations applying randomly injected faults.

A novel ensemble learning approach for fault detection of sensor data in cyber-physical system

Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This research paper presents a novel approach for enhancing fault detection in sensor data within a cyber-physical system through the integration of machine learning algorithms. Specifically, a hybrid ensemble methodology is proposed, combining the strengths of AdaBoost and Random Forest with Rocchio’s algorithm, to achieve robust and accurate fault detection. The proposed approach operates in two phases. In the first phase, AdaBoost and Random Forest classifiers are trained on a diverse dataset containing normal and faulty sensor data to develop individual base models. AdaBoost emphasizes misclassified instances, while Random Forest focuses on capturing complex interactions within the data. In the second phase, the outputs of these base models are fused using Rocchio’s algorithm, which exploits the similarities between faulty instances to improve fault detection accuracy. Comparative analyses are conducted against individual classifiers and other ensemble methods to validate the effectiveness of the hybrid approach. The results demonstrate that the proposed approach achieves superior fault detection rates. Additionally, the integration of Rocchio’s algorithm significantly contributes to the refinement of the fault detection process, effectively leveraging the strengths of AdaBoost and Random Forest. In conclusion, this research offers a comprehensive solution to enhance fault detection capabilities in cyber-physical systems by introducing a novel ensemble framework. By synergistically combining AdaBoost, Random Forest, and Rocchio’s algorithm, the proposed methodology provides a robust mechanism for accurately identifying sensor data anomalies, thus bolstering the reliability and performance of cyber-physical systems across a multitude of critical applications.

Advanced Process Monitoring Through Fault Detection and Classification for the Process Development of Tantalum Nitride Thin-Film Resistors

This paper discusses the optimization of an advanced process monitoring scheme with interdicting fault detection and classification (FDC) capabilities that improved the control over the process development of Tantalum Nitride thin film resistors (TaN TFR). Its implementation in a high-volume manufacturing environment resulted in a reduction of misprocessed wafers, shorter equipment downtime, higher throughput, enhanced process visibility, and yield improvement. Along with optimizing FDC capabilities, implementing a short loop sampling plan reduced the turnaround time for early electrical characterization by a factor of four; this allowed for timely inline adjustments within the fabrication process to tighten the statistical process control (SPC) over the distribution of the process control monitor (PCM) TaN resistor value and improve thin film uniformity.

Performance Analysis of Interval Type-2 Fuzzy X¯ and R Control Charts

Statistical process control (SPC) is one of the most powerful techniques for improving quality, as it is able to detect special causes of problems in processes, products and services with a remarkable degree of accuracy. Among SPC tools, X¯ and R control charts are widely employed in process monitoring. However, scenarios involving vague, imprecise and even subjective data require a type-2 fuzzy set approach. Thus, X¯ and R control charts should be coupled with interval type-2 triangular fuzzy numbers (IT2TFN) in order to add further information to traditional control charts. This paper proposes a performance analysis of IT2TFN and X¯ and R control charts by means of average run length (ARL), standard deviation of the run length (SDRL) and RL percentile. Computer simulations were carried out considering 10,000 runs to obtain ARL, SDRL and the 5th, 25th, 50th, 75th and 95th RL percentiles. Simulation results reveal that the proposed control charts increased fault detection capability (speed of response) and slightly reduced the number of false alarms in processes under control. Moreover, it was observed that, in addition to superior performance, IT2TFN X¯-R control charts proved to be more robust and flexible when compared to traditional control charts.

Research on an Intelligent Piezoelectric Needle Selector System with Closed-Loop Fault Detection Capability

The piezoelectric needle selector is a crucial component of computerized dobby weft knitting machines. With the continuous development of weft knitting machine technology, enhancing the accuracy of piezoelectric needle selector control is essential. Accurate determination of whether the blades are in the correct position would significantly improve the precision of piezoelectric needle selector control. In this study, piezoelectric ceramic sensors were used to collect impact vibration signals when the blades struck the damper baffle. Key hardware circuits were designed for this purpose. A self-learning algorithm was employed to capture the highest point and the time it takes to reach the highest point in the impact vibration signal. A fault detection algorithm was used to implement closed-loop fault detection for piezoelectric needle selectors. Experimental results and practical applications have demonstrated that this research effectively addresses the accurate determination of whether the piezoelectric needle selector blades are in the correct position. It has reduced the defect rate of fabric production in weft knitting, thereby improving the overall efficiency and profitability of businesses.

Identifying metamorphic relations: A data mutation directed approach

SummaryMetamorphic testing (MT) is an effective technique to alleviate the test oracle problem. The principle of MT is to detect failures by checking whether some necessary properties, commonly known as metamorphic relations (MRs), of software under test (SUT) hold among multiple executions of source and follow‐up test cases. Since both the generation of follow‐up test cases and test result verification depend on MRs, the identification of MRs plays a key role in MT, which is an important yet difficult task requiring deep domain knowledge of the SUT. Accordingly, techniques that can direct a tester to identify MRs effectively are desirable. In this paper, we propose MT, a data mutation directed approach to identifying MRs. MT guides a tester to identify MRs by providing a set of data mutation operators and template‐style mapping rules, which not only alleviates the difficulties faced in the process of MR identification but also improves the identification effectiveness. We have further developed a tool to implement the proposed approach and conducted an empirical study to evaluate the MR identification effectiveness of MT and the performance of MRs identified by MT with respect to fault detection capability and statement coverage. The empirical results show that MT is able to identify MRs for numeric programs effectively, and the identified MRs have high fault detection capability and statement coverage. The work presented in this paper advances the field of MT by providing a simple yet practical approach to the MR identification problem.

Multi-objective test selection of smart contract and blockchain applications.

The ability to create decentralized applications without the authority of a single entity has attracted numerous developers to build applications using blockchain technology. However, ensuring the correctness of such applications poses significant challenges, as it can result in financial losses or, even worse, a loss of user trust. Testing smart contracts introduces a unique set of challenges due to the additional restrictions and costs imposed by blockchain platforms during test case execution. Therefore, it remains uncertain whether testing techniques developed for traditional software can effectively be adapted to smart contracts. In this study, we propose a multi-objective test selection technique for smart contracts that aims to balance three objectives: time, coverage, and gas usage. We evaluated our approach using a comprehensive selection of real-world smart contracts and compared the results with various test selection methods employed in traditional software systems. Statistical analysis of our experiments, which utilized benchmark Solidity smart contract case studies, demonstrates that our approach significantly reduces the testing cost while still maintaining acceptable fault detection capabilities. This is in comparison to random search, mono-objective search, and the traditional re-testing method that does not employ heuristic search.

On the design of a small scale tidal converter for long time deployment at sea

A scale model of a kite-like converter of tidal energy, the GEMSTAR, is designed to be installed at sea for a long term deployment. The aim of the experiment is to develope a digital twin with fault detection and isolation capabilities. To this aim, a fully-functional 1:10 scale of GEMSTAR is designed, starting from measurements of tidal currents in the site of installation. Several spots were investigated searching for the most suitable current profile. The paper describes the principal issues concerning operational functions and the choice of the physical parameters to monitor during the deployment through several sensors placed on board. A scale model of a kite-like converter of tidal energy, the GEMSTAR, is designed to be installed at sea for a long term deployment. The aim of the experiment is to develope a digital twin with fault detection and isolation capabilities. To this aim, a fully-functional 1:10 scale of GEMSTAR is designed, starting from measurements of tidal currents in the site of installation. Several spots were investigated searching for the most suitable current profile. The paper describes the principal issues concerning operational functions and the choice of the physical parameters to monitor during the deployment through several sensors placed on board.

LoFFT: Low-Voltage FFT Using Lightweight Fault Detection for Energy Efficiency

Operating at reduced voltage is an effective technique for improving the energy efficiency of computing. However, the approach is constrained by its exacerbated sensitivity to Process, Voltage, and Temperature (PVT) variations, which under throughput constraints challenges finding the energy minimizing voltage-frequency operating point. Commonly utilized design approaches for adaptive voltage scaling are based on timing slack measurement or speculation techniques that require adding extra hardware, e.g., Error Detection Sequence (EDS) circuits, that substantially increase the design complexity, and are not applicable for already fabricated designs. In this paper, instead of circuit-level techniques, a low-cost algorithmic error detection method is proposed as the enabler for reduced voltage operation of Fast Fourier Transform (FFT) accelerators.Without requiring neither gate-level nor circuit-level modifications, the method works based on an intrinsic property of the Fourier transform, i.e., Parsevalfs identity. The method is demonstrated on a System-on-Chip (SoC) that integrates a Field-Programmable Gate Array (FPGA) made to operate at reduced voltages. The fault detection capability is profiled using the demonstration test bench, implemented both as software and as hardware. In the experiments, a.43% reduction in power consumption was achieved without sacrificing the throughput and reliability. The overheads of the proposed fault detection approach scale sub-linearly with respect to FFT size and are ≤10% for 1024-point FFT.

Storage-Based Logic Built-In Self-Test With Cyclic Tests

Logic built-in self-test (LBIST) eliminates the need for external test data, and thus facilitates in-field testing. Instead of pseudo-random tests that are typically produced by on-chip test generation logic, storage-based LBIST uses deterministic test data entries, which are stored on-chip, for applying tests that are closer to deterministic tests. This article describes a storage-based LBIST approach that uses a unique type of scan-based tests referred to as cyclic tests. Cyclic tests have cyclic scan enable and scan in sequences, with a higher proportion of functional capture cycles compared with conventional scan-based tests. This results in more clock cycles where fault effects can be captured in the flip-flops. The improved fault detection capabilities this provides helps balance the number of applied tests and the storage requirements. Experimental results for benchmark circuits demonstrate the effectiveness of cyclic tests.

A novel semi-supervised fault detection and isolation method for battery system of electric vehicles

The detection and isolation of early and minor faults in vehicle battery systems is vital to safe driving and improving power utilization. This paper proposes a data-driven model to achieve accurate, early, and economical fault detection and isolation. The model is based on kernel principal component analysis (KPCA), which maps complex nonlinear data from the input space into a high-dimensional feature space to gain a detection model with good performance. To overcome the difficulty of hyperparameter selection, KPCA is trained using Bayesian Optimization (BO) iterations with a small amount of labeled data and a large amount of unlabeled data. This step can obtain the optimal hyperparameter to greatly improve the model fault detection capability, which is beneficial for detecting both early faults and minor faults. In addition, a unified contribution graph based on the partial differentiation of KPCA was adopted to build a reasonable isolation scheme. The semi-supervised model of KPCA based on Bayesian Optimization and contribution graph is developed to reveal the relationship between fault and variable. Finally, the proposed method is fully tested on four fault datasets and the results prove the excellent detection capability in the early stage of faults compared with other methods and the accurate fault isolation capability from the occurrence to the end of the fault.

Accuracy Is Not Enough: Optimizing for a Fault Detection Delay

This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so.

Construction of Data-Driven Performance Digital Twin for a Real-World Gas Turbine Anomaly Detection Considering Uncertainty.

Anomaly detection and failure prediction of gas turbines is of great importance for ensuring reliable operation. This work presents a novel approach for anomaly detection based on a data-driven performance digital twin of gas turbine engines. The developed digital twin consists of two parts: uncertain performance digital twin (UPDT) and fault detection capability. UPDT is a probabilistic digital representation of the expected performance behavior of real-world gas turbine engines operating under various conditions. Fault detection capability is developed based on detecting UPDT outputs that have low probability under the training distribution. A novel anomaly measure based on the first Wasserstein distance is proposed to characterize the entire flight data, and a threshold can be applied to this measure to detect anomaly flights. The proposed UPDT with uncertainty quantification is trained with the sensor data from an individual physical reality and the outcome of the UPDT is intended to deliver the health assessment and fault detection results to support operation and maintenance decision-making. The proposed method is demonstrated on a real-world dataset from a typical type of commercial turbofan engine and the result shows that the F1 score reaches a maximum of 0.99 with a threshold of 0.45. The case study demonstrated that the proposed novel anomaly detection method can effectively identify the abnormal samples, and it is also possible to isolate anomalous behavior in a single performance signal, which is helpful for further fault diagnosis once an anomaly is detected.

Supervision System 4.0 for a Road Tanker Washing Robot Manipulator

The washing of road tankers is currently still a manual process that requires an operator to place the washing head into the tanks. To increase productivity and operator safety, it is essential to implement automated systems with Fault Detection and Isolation (FDI) capabilities. On the other hand, the industry 4.0 paradigm promotes the use of collaborative systems that integrate with the other organization’s processes. Realizing this new vision requires Supervision, Control and Data Acquisition (SCADA) systems with FDI modules that integrate with collaborative systems and promote the digitalization of companies. This paper proposes a SCADA system for a new road tanker washing robot, aimed at integrating all systems and communication networks of the organization and future FDI modules. To this end, this paper proposes a communication architecture based on open protocols and a common database to connect SCADA to the lower and higher levels of automation. Furthermore, this paper describes the various aspects of SCADA system development, from synoptic design to validation. To support the development of the SCADA system, a Digital Twin (DT) of the road tanker washing robot was used, allowing to test and validate its functionality through this virtual prototype. The results show that the proposed SCADA system and underlying information architecture are suitable for supervision of a robotized wash operation and that the use of a Digital Twin facilitates SCADA system design and validation.

Using expression parsing and algebraic operations to generate test sequences.

It has become a popular trend to build software’s regular expression or extended regular expression models in order to generate test sequences from these models. Such test sequences tend to have promising test coverage and fault detection capability. During this process, one critical step is expression parsing based on algebraic operations. However, the parsing can be very challenging as different algebraic systems have different algebraic operators and algebraic operations. Besides, the parsing difficulty continues to grow as software complexity increases. To address the above challenges, this paper proposes a general expression parsing framework for test sequence generation. The proposed framework consists of three stages, expression decomposition, algebraic operations, and subexpression combination. To implement the framework, an expression parsing algorithm based on abstract syntax tree is developed. Case studies based on 117 expressions collected from the literature over the past 30 years as well as 13 software systems are conducted to evaluate the effectiveness of the proposed algorithm. The results indicate that our algorithm is superior to three existing and commonly used algorithms with respect to expression parsing and software fault detection.

Compact Functional Testing for Neuromorphic Computing Circuits

We address the problem of testing Artificial Intelligence (AI) hardware accelerators implementing Spiking Neural Networks (SNNs). We define a metric to quickly rank available samples for training and testing based on their fault detection capability. The metric measures the inter-class spike count difference of a sample for the fault-free design. In particular, each sample is assigned a score equal to the spike count difference between the first two top classes. The hypothesis is that samples with small scores achieve high fault coverage because they are prone to misclassification, i.e., a small perturbation in the network due to a fault will result in these samples being misclassififed with high probability. We show that the proposed metric correlates with the per-sample fault coverage and that retaining a set of high-ranked samples in the order of ten achieves near perfect fault coverage for critical faults that affect the SNN accuracy. The proposed test generation approach is demonstrated on two SNNs modelled in Python and on actual neuromorphic hardware. We discuss fault modeling and perform an analysis to reduce the fault space so as to speed up test generation time.

Research on short-circuit fault-diagnosis strategy of lithium-ion battery in an energy-storage system based on voltage cosine similarity

In recent years, accidents such as spontaneous combustion and explosion have frequently occurred in the field of electrochemical energy storage, and thermal runaway caused by short-circuit faults in lithium-ion (Li-ion) batteries is one of the main reasons. This study investigated the internal short circuit (ISC) fault diagnosis method for Li-ion (LiFePO4) batteries in energy storage devices. A short-circuit fault diagnosis method for battery module components based on voltage cosine similarity is proposed based on the characteristics extracted from the ISC fault battery. In this method, the voltage and current of the battery were used to derive a two-dimensional feature vector, and a gain multiple with excitation information was introduced to perform secondary processing on the eigenvectors and achieve effective separation of the fault-signal features. The experimental results show that the proposed method can achieve a second-level rapid diagnosis of short-circuit faults in batteries when the simulated ISC resistance is below 5 Ω, and the excellent fault-detection capabilities for modules with inconsistent states of charge and the performance of the proposed method under dynamic conditions are verified.

Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

AbstractThe overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block‐based attention mechanism stacked autoencoder (MB‐AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self‐attention is used to focus on the information of each block itself. Cross‐attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee‐Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB‐AMSAE algorithm in process monitoring.

Application of metamorphic testing on UAV path planning software

Both the performance and reliability evaluation processes of the unmanned aerial vehicle path planning software rely on the determination of the correctness of the execution results of unmanned aerial vehicle path planning software. However, this task is hindered due to the testing oracle problem. In this paper, a framework is designed to overcome the oracle problem and verify the correctness of path planning software based on the grid searching algorithms. In this framework, a metamorphic testing-based method is proposed, and three operations-based metamorphic relations are proposed and proved towards the target software. While analysis of the software is conducted, the version with manually injected faults as well as the officially released version are both dealt with. It is shown that in the experimental results the injected faults can be effectively revealed by using the methods proposed in this paper. Besides, through the evaluation of different types of metamorphic relations, we find that the composed metamorphic relations have stronger fault detection capability compared to the individual ones.

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults. In this paper, a deep learning-based observer, which combines the convolutional neural network (CNN) and the long short-term memory network (LSTM), is employed to approximate the nonlinear driving control system. CNN layers are introduced to extract dynamic features of the data, whereas LSTM layers perform time-sequential prediction of the target system. In terms of application, normal samples are fed into the observer to build an offline prediction model for the target system. The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs. Online fault detection can be realized by analyzing the residuals. Finally, an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme. Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.