Faulty Operation Research Articles

A Novel Generic Diagnosis Algorithm in the Time Domain Representation

The health monitoring of a system remains a major issue for its lifetime preservation. In this paper, a novel fault diagnosis algorithm is proposed. The proposed diagnosis approach is based on a unique variable measurement in the time domain and manages to extract the system behavior evolution. The developed tool aims to be generic to several physical systems with low or high dynamic behavior. The algorithm is depicted in the present paper and two different applications are considered. The performance of the novel proposed approach is experimentally evaluated on a fan considering two different faulty conditions and on a proton exchange membrane fuel cell. The experimental results demonstrated the high efficiency of the proposed diagnosis tool. Indeed, the algorithm can discriminate the two faulty operation modes of the fan from a normal condition and also manages to identify the current system state of health. Regarding the fuel cell state of health, only two conditions are tested and the algorithm is able to detect the fault occurrence from a normal operating mode. Moreover, the very low computational cost of the proposed diagnosis tool makes it especially suitable to be implemented on a microcontroller.

Deep learning method based on autoencoder neural network applied to faults detection and diagnosis of photovoltaic system

The paper presents an application of deep learning for fault detection in PV systems located in Algiers –Algeria which has a nominal power of 9.54 kW. Each PV array comprises 30 PV modules each string contains 15 PV modules in series. Fault detection and diagnosis of Photovoltaic Systems (PVS) is an important task for successful solar power generation. Several faults have been occurred such as short-circuit cases and open-circuit string cases in PV generator can leads to fire and risk. The design of efficient defect detection methods for PV systems presents a challenge especially for small scale PV farms. Faults detection methods are failed under the presence of noisy signals. This paper presents a deep learning (DL) based method for detection, diagnosis and classification of the aforementioned defects. The proposed procedure consists of four fundamental steps, first a heuristic optimization approach based on Coyote Optimization Algorithm (COA) the five-unknown electrical parameters of the One Diode Model (ODM) and their insertion into a PSIM-based simulation that aims to mimic the operating PV system. Second, database construction that includes current, voltage and power at the Maximum Power Point (MPP), module temperature and solar irradiance for the PV system is supposed under healthy and faulty operations condition at optimal operational conditions. Then, this is followed by the extraction of new features of the old database using unsupervised learning characteristics of the Auto-Encoder (AE). and last, supervised learning using the new database based on Artificial Neural Network (ANN) construction for PV fault detection and classification. The proposed technique has been validated using monitored data from a real operating PV system situated in Algeria. The obtained results have shown the effectiveness of the proposed technique detection and classification of various PV fault types.

Online Sensitive Turn-to-Turn Fault Detection in Power Transformers

Power transformers are of the most critical and expensive equipment in the power system industry. Meanwhile, they are exposed to a variety of faulty and abnormal operating conditions. Therefore, to prevent extension of these events they are protected by different protective schemes. Among the various types of the transformer faults, detecting turn-to-turn faults is the most challenging one for the protection systems. Industrial experiences and standard documents reveal that the existing protection systems include severe shortcomings, and still more efforts are required to offer a reliable method for detecting such faults. To this end, this article puts forward the proposal of a novel turn-to-turn fault detection method to satisfy the basic protection requirements of dependability, security, and speed. This method properly detects the low-level turn-to-turn faults while is stable during system transients and external faults. Performance of the proposed method is evaluated through various experimental and simulation studies. The obtained results verify superior operation of the proposed method under different faults and conditions.

Performance of a vector controlled PMSM drive based on proportional–resonance control under single-phase open circuit fault

In this paper, a vector controlled PMSM drive based on a proportional–resonance controller (PR) under open circuit fault (OCF) is proposed. The PR controller is used to generate the stator voltages. While the field-oriented control (FOC) approach has been implemented using the pulse width modulation (PWM) technology. The simulation is built using MATLAB/ Simulink. Hardware experiments were performed, and the results captured using dSPACE DS1104. The behaviour of the PMSM is studied under three operations: healthy, faulty, and post-fault operation. Moreover, the speed control of the PMSM under the three operations has been presented. The simulation and the experimental results show a fast dynamic response with some ripple in the response of speed and torque during the healthy and post-fault operations. On the other hand, during the OCF, the ripple in the torque and speed response increases. However, the proposed method proves its efficacy by keeping the motor speed nearly at the input reference speed as well as guaranteeing the continued operation of the PMSM with good steady-state and dynamic performance.

Explainable root cause and pathway analysis with robust and adaptive statistics

Accurate detection of faults is desired to reduce risks and costs. The identification of the propagation path of faults and the system’s variables responsible of faulty operating conditions is also paramount. This paper presents a new alternative to a well-known Bayesian network-based approach to detect and identify root causes in multivariate processes. It deals with the complexity generated by the use of Bayesian network in terms of structure and decision making. The new strategy is straightforward and based on statistical foundations. The new approach revives the interest of Mason, Young and Tracy (MYT) decomposition of quadratic statistics and alleviates considerably the complexity of their upgrades. Comparison with previous approaches and performance evaluation using the Tennessee Eastman process demonstrate the feasibility and interest of the new proposal.

Fault-Tolerant Torque Controller Based on Adaptive Decoupled Multi-Stator Modeling for Multi-Three-Phase Induction Motor Drives

Among the multiphase solutions, multi-three-phase drives are becoming more and more widespread in practice as they can be modularly supplied by conventional three-phase inverters. The literature reports several control approaches to perform the torque regulation of multi-three-phase machines. Most of such solutions use the vector space decomposition (VSD) approach since it allows the control of a multi-three-phase machine using the conventional control schemes of three-phase drives, thus reducing the complexity of the control algorithm. However, this advantage is practically lost in the case of open-three-phase faults. Indeed, the postfault operation of the VSD-based drive schemes requires the implementation of additional control modules, often specifically designed for the machine under consideration. Therefore, this article aims to propose a novel control approach that allows using any control scheme developed for three-phase motors to perform the torque regulation of a multi-three-phase machine both in healthy and faulty operation. In this way, the previously mentioned drawbacks of the VSD-based control schemes in dealing with the faulty operation of the machine are avoided. Moreover, the simplicity of the control algorithm is always preserved, regardless of the machine's operating condition. The proposed solution has been experimentally validated through a 12-phase induction motor, rated 10 kW at 6000 r/min, using a quadruple-three-phase configuration of the stator winding.

Diagnosis of Broken Rotor Bars in Six-Phases Squirrel Cage Induction Motor

Multiphase machines are increasingly used for reasons of reliability and power segmentation. This paper proposes to study a common example of these machines, six-phase squirrel cage induction motor. The study focuses on the broken bars fault when the motor is supplied by voltage source inverter. The originality of this work lies in the application of diagnostic techniques in a non-conventional machine such as six-squirrel cage induction motor. Therefore, this paper discusses the effect of the broken rotor bar fault in the induction six-phase squirrel cage motor. The drive of this motor requires a multiphase power supply. This is the reason why a six-phases inverter has been built to ensure the healthy or faulty operation of this motor. In order to study the rotor fault, a test bench contains several rotors with rotor broken bars. In order to detect this fault, Motor Current Signature Analysis (MCSA) has become the most common method in the field due to its simplicity, its low computation time, and remote monitoring capability this why this technique is used in this work. The experimental results show the interest and the efficiency of the technique used for the diagnosis of rotor bar faults in the six-phase squirrel cage induction motor.

Experimental studies of air-handling units’ faulty operation for the development of data-driven fault detection and diagnosis tools: A systematic review

Automated Fault Detection and Diagnosis (AFDD) algorithms could represent one of the most effective solutions in order to reduce energy demand, greenhouse gas emissions and running costs of Heating, Ventilation and Air-Conditioning (HVAC) systems equipped with Air-Handling Units (AHUs). In particular, data-driven AFDD tools are recognized as easier to be developed and able to provide a higher accuracy with respect to other AFDD tools. However, they are still in the early stage of adoption stock-wide mainly due to the facts that data-driven AFDD models (i) require labeled and reliable experimental faulty data that are time-consuming and expensive to be obtained under different operating scenarios, and (ii) cannot operate beyond the training data. In this paper the most significant scientific papers focusing on experimental analyses of AHUs aiming at the development of data-driven AFDD algorithms have been systematically reviewed and categorized in order to highlight the most important research gaps to be still covered. In particular, the AHU operating schemes, fault types, faults severities and climatic conditions requiring further studies have been identified with the main aim of supporting and guide the future development of new and accurate data-driven AFDD systems.

Faulty Operation of Coils’ and Humidifier Valves in a Typical Air-Handling Unit: Experimental Impact Assessment of Indoor Comfort and Patterns of Operating Parameters under Mediterranean Climatic Conditions

Data-driven Automated Fault Detection and Diagnosis (AFDD) are recognized as one of the most promising options to improve the efficiency of Air-Handling Units (AHUs). In this study, the field operation of a typical single-duct dual-fan constant air volume AHU is investigated through a series of experiments carried out under Mediterranean (southern Italy) climatic conditions considering both fault-free and faulty scenarios. The AHU performances are analyzed while artificially introducing the following five different typical faults: (1) post-heating coil valve stuck at 100% (always open); (2) post-heating coil valve stuck at 0% (always closed); (3) cooling coil valve stuck at 100% (always open); (4) cooling coil valve stuck at 0% (always closed); (5) humidifier valve stuck at 0% (always closed). The measured faulty data are compared against the corresponding fault-free performance measured under the same boundary conditions with the aim of assessing the faults’ impact on both thermal/hygrometric indoor conditions, as well as patterns of 16 different key operating parameters. The results of this study can help building operators and facility engineers in identifying faults’ symptoms in typical AHUs and facilitate the related development of new AFDD tools.

Chance-constrained model predictive control-based operation management of more-electric aircraft using energy storage systems under uncertainty

On more electric aircraft (MEA), reducing fuel consumption and guaranteeing flight safety are pursued by efficient operational management of the electrical power system (EPS). Considering the growing number of onboard electric loads and the increasing complexity of EPS architecture due to the integration of multiple power converters and energy storage systems (ESSs), system-level operation control is required to manage power distribution, load scheduling, and ESSs. In this paper, a chance-constrained stochastic model predictive control (CC-SMPC) method is proposed to improve both the system operation in terms of the system's cost and reconfiguration activities as well as the ability to cope with uncertainties due to fluctuating load demands. Both normal and faulty operating conditions are investigated with multi-failure cases, resulting in different uncertainty propagation paths. The system's operational and technical requirements are formulated as a set of deterministic and probabilistic constraints in the CC-SMPC model. To verify the effectiveness of the proposed strategy, a comprehensive comparison study is conducted. Two uncertainty/failure cases are taken into account and simulations are performed for both offline and online control strategies while the Monte-Carlo algorithm is used for scenario generation. The results are evaluated using the proposed evaluation framework, showing that the CC-SMPC achieves better performance compared to deterministic MPC (DMPC) in both cases. In an offline testing framework, comparing the performance of DMPC and CC-SMPC strategies shows that CC-SMPC reduces the power constraint violations for batteries and generators in all cases following the selected confidence level. In addition, in an online testing framework with 1 % violation probability, the following results are observed in the two cases: In the EPS normal condition, CC-SMPC reduces the total cost by 31.4 % and the overall constraint violation cost by 93 %; while in the EPS faulty condition, CC-SMPC reduces the total cost by 4.37 %, and the overall constraint violation cost by 96 %.

Application of power spectral density and the support vector machine to fault diagnosis for permanent magnet synchronous motor

The present study proposes a new technique for diagnosing the Inter-Turn Stator-Winding fault in Permanent Magnet Synchronous Motors. Under faulty operating conditions, the system’s model is built while considering specific parameters linked to fault location and sharpness. The proposed fault-detection method is principally based on the Power Spectral Density estimator’s association with the Support Vector Machine. This classifier is used to separate different regions of system performance. It has been trained to associate the Power Spectral Density current’s magnitude and the stator’s current negative sequence with the fault severity. This method has shown exceptional performance as long as it achieves a fault detection rate of 98.5% for different fault severities. Two Power Spectral Density estimators were compared in terms of their ability to extract fault characteristics, namely Burg’s method and Welch’s method. It was concluded from this study that Welch’s has a higher frequency resolution than Burg’s method.

Understanding and managing hazards of lithium‐ion battery systems

AbstractOver the last decade, the rapid development of lithium‐ion battery (LIB) technology has provided many new opportunities for both Energy Storage Systems (ESS) and Electric Vehicle (EV) markets. At the same time, fire and explosion risks associated with this type of high‐energy battery technology have become a major safety concern. Many advances have been made in understanding reactive chemistry and fire‐safety issues related to both thermal runaway and fire hazards presented by LIBs. Thermal runaway or fire can occur from battery manufacturing defects, charging system malfunctions, extreme abuse conditions that may result from a faulty operation or traffic accidents, and end‐of‐life battery handling. Failure of the battery is often accompanied by the release of toxic gas, fire, jet flames, and explosion hazards, which present unique exposures to workers and emergency response personnel. LIB fires often present complex emergency response challenges, requiring extensive amounts of water applied over several hours to cool batteries, extinguish the fire, and prevent reignition. This paper overviews the fundamental principles required to establish a basis of safety for proper storage, handling, and use of LIBs. Starting with an overview of the technology used in LIB systems, the paper will provide a review of common sources for thermal runaways and fire events and practical ways used to prevent their occurrence. While regulatory coverage for LIB storage, handling, and use is still in various stages of development, a growing body of international best practices and consensus views have emerged. Finally, to safely address thermal runaway and fire events, an overview of global best practices for emergency response activities involving LIBs is provided.

Noise and Vibration Prediction of a Six-Phase IPMSM in a Single Open-Phase Failure Under a Negative Sequence Current Compensated Fault Tolerant Control Mode

Due to a multiphase configuration, six-phase interior permanent magnet synchronous machines (IPMSMs) exhibit the inherent reliability and fault-tolerant capabilities. Using a fault-tolerant control (FTC) method, the machine is still able to deliver the desired torque under the open-phase fault with a potential degradation in its efficiency and vibration behavior. The vibroacoustic characteristics of the IPMSM under the faulty operation during the FTC mode have not been studied in the literature. To fill this knowledge gap, the simulation and experiment-based noise and vibration (NV) analysis of a six-phase IPMSM in a single open-phase condition using a negative sequence current-compensated FTC mode is studied in this article. First, the whole procedure of the NV analysis for the IPMSM model is introduced. The calculation of the electromagnetic (EM) force is then studied to explore the main excitation of the vibration in the system. After this, the NV synthesis is carried out and the simulation results of the NV under a negative sequence current-compensated FTC mode are obtained. It is found that the 28th order of the flux density spatial harmonics component influences vibration the most. Finally, the NV predictions in both healthy mode and controlled faulty mode are verified by experiments.

Automated Fault Tree Learning from Continuous-valued Sensor Data

Many industrial sectors have been collecting big sensor data. With recent technologies for processing big data, companies can exploit this for automatic failure detection and prevention. We propose the first completely automated method for failure analysis, machine-learning fault trees from raw observational data with continuous variables. Our method scales well and is tested on a real-world, five-year dataset of domestic heater operations in The Netherlands, with 31 million unique heater-day readings, each containing 27 sensor and 11 failure variables. Our method builds on two previous procedures: the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning algorithm from Boolean data. C4.5 pre-processes each continuous variable: it learns an optimal numerical threshold which distinguishes between faulty and normal operation of the top-level system. These thresholds discretise the variables, thus allowing LIFT to learn fault trees which model the root failure mechanisms of the system and are explainable. We obtain fault trees for the 11 failure variables, and evaluate them in two ways: quantitatively, with a significance score, and qualitatively, with domain specialists. Some of the fault trees learnt have almost maximum significance (above 0.95), while others have medium-to-low significance (around 0.30), reflecting the difficulty of learning from big, noisy, real-world sensor data. The domain specialists confirm that the fault trees model meaningful relationships among the variables.

Experimental dataset for an AHU air-to-air heat exchanger with normal and simulated fault operations

Fault Detection and Diagnosis (FDD) is an important tool in building commissioning. Providing a consolidated dataset for FDD benchmarking is necessary to accurately evaluate the FDD prediction accuracy and detect anomalies. In this study, we provide an experimental dataset for an air handling unit containing two ducts linked by an air-to-air heat exchanger. The dataset is composed of nominal and faulty operations of the system, including the ground truth in order to investigate various faults in 52 cases. The dataset was obtained by measuring a representative system with real climate variations like the ones obtained by Building Automation Systems. The transition between nominal and fault sequences was continuous, as in real operating conditions. An uncertainty evaluation was carried out to provide confidence bounds in the experimental dataset.

1. System monitorowania zagrożeń pyłowych i akustycznych w zakładach przemysłowych wykorzystujący mierniki niskokosztowe

Among the harmful factors related to the work environment, noise and dust poses the greatest threat. Continuous monitoring of working environment parameters may be an effective solution to this problem, enabling the quick detection of areas with high noise emissions and dust concentration and their sources. Thanks to the proposed solution, OSH services will receive a tool for a quick response to exceeded exposure limit values for factors harmful to health. Such solution will also facilitate the assessment of technical conditions of installation, because excessive dust emission and/or high levels of exposure to noise may be the result of unsealing, or faulty operation of devices. The results of the meter prototype for PM2.5 concentrations of suspended dust are satisfactory. Comparative measurements of A-sound levels for different test signal levels confirmed small differences in relation to the reference meter. It was found that the prototype can be used as a tool for a quick and effective response to exceeded exposure limit values for dust and noise in an industrial plant. This article discusses the problems of occupational safety and health, which are covered by health sciences and environmental engig.

Gated-CNN: Combating NBTI and HCI aging effects in on-chip activation memories of Convolutional Neural Network accelerators

Negative Bias Temperature Instability (NBTI) and Hot Carrier Injection (HCI) are two of the main reliability threats in current technology nodes. These aging phenomena degrade the transistor’s threshold voltage (Vth) over the lifetime of a digital circuit, resulting in slower transistors that eventually lead to a faulty operation when the critical paths become longer than the processor cycle time. Among all the transistors on a chip, the most vulnerable transistors to such wearout effects are those used to implement SRAM storage, since memory cells are continuously degrading. In particular, NBTI ages PMOS cell transistors when a given logic value is stored for a long period (i.e., a long duty cycle), whereas HCI ages NMOS cell transistors not only when the stored value flips but also when it is accessed. This work focuses on mitigating aging in the on-chip SRAM memories of Convolutional Neural Network (CNN) accelerators storing activations. This paper makes two main contributions. At the software level, we quantify the aging induced by current CNN benchmarks with a characterization study of duty cycle, flip, and access patterns in every activation memory cell. Based on the insights from this study, this work proposes a novel microarchitectural technique, Gated-CNN, that ensures a uniform aging degradation of every memory cell. To do so, Gated-CNN exploits power-gating and address rotation techniques tailored to the memory demands and temporal/spatial localities exhibited by CNN applications, as well as the memory organization and management of CNN accelerators. Experimental results show that, compared to a conventional design, the average Vth degradation savings are at least as much as 49% depending on the type of transistor.

Fault Tolerance Analysis of Five-Level Neutral-Point-Clamped Inverters under Clamping Diode Open-Circuit Failure

Multilevel inverters are increasingly used in industrial applications in which service continuity is crucial. This paper presents an original approach to ensure the fault-tolerant operation of neutral-point-clamped (NPC) inverters in the case of an open-circuit fault event in a clamping diode, which is rarely investigated in the conducted research work. The proposed fault-tolerant strategy meets the following criteria: restoring rated output voltage and current during post-fault operation, realizing fault-tolerant operation without additional components, maintaining rated total harmonic distortion (THD) during fault-tolerant operation, and fast transition between faulty operation and remedial operation. In the proposed approach, prior to applying the appropriate remedial action, identification of the defective clamping diode is required. In this respect, a very fast and simple logic-based fault diagnosis is presented whose implementation does not need any additional components and external circuit. Moreover, it does not require any component modeling and complicated calculations. The proposed strategy is validated by simulation and experimentation.

Equipment activity recognition and early fault detection in automated construction through a hybrid machine learning framework

AbstractExisting studies on automated construction equipment monitoring have focused mainly on activity recognition rather than fault detection. This paper proposes a novel equipment activity recognition and fault detection framework called hybrid unsupervised and supervised machine learning (HUS‐ML). HUS‐ML first identifies normal operations and known faulty conditions through supervised learning. Then, an anomaly detection algorithm is applied to spot any unseen faulty conditions. The framework is tested using acceleration measurements from a low‐rise automated construction system prototype. HUS‐ML outperformed the conventional machine learning approach in activity recognition and fault detection with an average F1 score of 86.6%. The conventional approach failed to detect unseen faulty operations. HUS‐ML identified known faulty operations and unseen faulty operations with F1 scores of 98.11% and 76.19%, respectively. The generalizability of the framework is demonstrated by validating it on an independent benchmark dataset with good results.

Velocity Sensor Fault-Tolerant Controller for Induction Machine Using Intelligent Voting Algorithm

Nowadays, induction machines (IMs) are widely used in industrial and transportation applications (electric or hybrid ground vehicle or aerospace actuators) thanks to their significant advantages in comparison to other technologies. Indeed, there is a large demand for IMs because of their reliability, robustness, and cost-effectiveness. The objective of this paper is to improve the reliability and performance of the three-phase induction machine in case of mechanical sensor failure. Moreover, this paper will discuss the development and proposal of a fault-tolerant controller (FTC), based on the combination of a vector controller, two virtual sensors (an extended Kalman filter, or EKF, and a sliding mode observer, or SMO) and a neural voting algorithm. In this approach, the vector controller is based on a new structure of a back-stepping sliding mode controller, which incorporates a double integral sliding surface to improve the performance of the induction machine in faulty operation mode. More specifically, this controller improves the machine performance in terms of having a fast response, fewer steady-state errors, and a robust performance in the existence of uncertainty. In addition, two voting algorithms are suggested in this approach. The first is based on neural networks, which are insensitive to parameter variations and do not need to set a threshold. The second one is based on fuzzy logic. Finally, validation is carried out by simulations in healthy and faulty operation modes to prove the feasibility of the proposed FTC.