Fault Diagnosis and Isolation for Diesel Engine Combustion Chambers Based on Autoencoder and BP Neural Network

Modeling and fault diagnosis design for HVAC systems using recurrent neural networks

THE LIGHT "KNIFE" METHOD FOR STUDYING AERODYNAMIC PROCESSES IN THE COMBUSTION CHAMBERS OF DIESEL ENGINES V. M. Boiko, O. G. Kudelin, O. N. Lebedev, G. E. Lushchaev, and A. A. Plesovskikh UDC 621.43-44 It is well known that in order to optimize the working process of internal combustion engines (ICEs) it is necessary to know the laws governing the motion of air in the combustion chambers (CCs) throughout the entire period of compression as a preparatory stage before mix- ing and burning of the fuel. This question has never been completely studied, though more than one generation of investigators in different countries has studied it intensively [1-5 and others]. In the opinion of many of these investigators the question can be reduced to two problems that reflect the main difficulty in the experimenal study: choice of measure- ment methods and organization of the optical access to the CC. Among the modern methods for performing measurements the methods based on contact-free optical principles must be singled out. Thus, compared with the traditional contact methods [6, 7], the contact-free methods inherently have a high accuracy, which is especially impor- tant, and the compression process is not disturbed. For this reason they can be used to study complicated aerodynamic processes in the CCs of ICEs. The light "knife" method stands out among the methods used in practice [8]. In this work this method is chosen because it has significant advantages over the other methods. The realization of optical access directly into the CC, situated in the piston, is an extremely difficult technical problem. We shall study the traditional methods for solving this problem for the following examples. In [9] a model of a piston whose bottom was made in the form of a transparent insert and the skirt, was equipped with a cut is described. A mirror was rigidly fastened inside the piston in the zone of the cut at an angle to its longi- tudinal axis. The cylinder contained an optically transparent window, opposite which a mo- tion picture camera was placed. The errors introduced by the refraction of the light rays on the curved surface as well as the great technical difficulties in preparing the apparatus make it difficult to use this model for studying the aerodynamic qualities of semidivided CC in diesel engines.

Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems

This research result consists of two parts: one is general theory on causality assignment for hybrid bond graph (HBG) and another is application of this concept to the quantitative fault diagnosis. From Low et al., 2008, a foundation for quantitative bond graph-based fault detection and isolation (FDI) design using HBG is laid. Useful causality properties pertaining to the HBG from FDI perspectives, and the concept of diagnostic hybrid bond graph (DHBG) which is advantageous for efficient and effective FDI applications are proposed. This paper is a continuation of our previous paper (Low et al., 2008). Here, the DHBG is exploited to analyze the hybrid system's fault detectability and fault isolability. Additionally, a quantitative FDI framework for effective fault diagnosis for hybrid systems is proposed. Simulation and experimental results are presented to validate some key concepts of the quantitative hybrid bond graph-based FDI framework.

Effective fault detection and isolation can improve the safety, reliability and efficiency of the district heating system. In order to detect and locate the sensor, actuator and component faults in the district heating system with faster response speed and higher accuracy, a two-level fault detection and isolation scheme, consisting of upper-level classifier for system faults and lower-level classifier for sub-faults, is developed based on convolutional neural networks. In consideration of the difficulty of obtaining the operation data of a real district heating system under various faulty states, a test benchmark model of an integrated energy based district heating system is built from the system level, which contains the renewable energy based water boilers, transmission networks, and heat load substations to examine the effectiveness of the proposed scheme. To improve the model reality, the dynamics of sensors and actuators, models of heat exchangers, and thermal inertia are considered in the district heating system, and Gaussian noises are added in the raw data signals. Nine kinds of system faults including sensor, actuator, component faults, along with three sub-fault types of bias, drift, complete failure, are investigated in the benchmark system. The performance of the proposed two-level fault detection and isolation scheme is evaluated under different data windows and Gaussian noises in the district heating system, and is compared with other data-driven methods including k-nearest neighbor, random forest and back propagation neural networks. Experimental results show that the two-level fault detection and isolation scheme can detect the faults in the district heating system accurately and robustly, and the proposed scheme has the potential to become an effective solution to real-time monitoring of faults in the district heating system.

This paper deals with the fault detection, diagnosis and control problems of time delay systems by designing a disturbance observer (DOB). In proposed method, the DOB estimates the changes occurred as an input disturbance. In fact, the DOB is implemented as a residual generator for the system. Subsequently, the estimated changes can be used to compensate the effect of faults and plant alterations directly. In this method, the estimated disturbances can be used for the FDI purpose and the final system would be fault tolerant, without the need to have all other faults isolable and identifiable. It provides a simple, low cost and effective fault detection and isolation method which can be applied to more practical systems. The applicability and effectiveness of the proposed method is illustrated by the simulations provided for an industrial water pressure control system which is supposed to be subject to faults.

In this paper, a new robust fault detection and isolation (FDI) methodology for an unmanned aerial vehicle (UAV) is proposed. The fault diagnosis scheme is constructed based on observer-based techniques according to fault models corresponding to each component (actuator, sensor, and structure). The proposed fault diagnosis method takes advantage of the structural perturbation of the UAV model due to the icing (the main structural fault in aircraft), sensor, and actuator faults to reduce the error of observers that are used in the FDI module in addition to distinguishing among faults in different components. Moreover, the accuracy of the FDI module is increased by considering the structural perturbation of the UAV linear model due to wind disturbances which is the major environmental disturbance affecting an aircraft. Our envisaged FDI strategy is capable of diagnosing recurrent faults through properly designed residuals with different responses to different types of faults. Simulation results are provided to illustrate and demonstrate the effectiveness of our proposed FDI approach due to faults in sensors, actuators, and structural components of unmanned aerial vehicles.

Sensor fault identification based on time-lagged PCA in dynamic processes

<div class="htmlview paragraph">The stochastic Fault Detection and Isolation (FDI) algorithm, known as the statistical local approach, is applied in a model-based framework to the diagnosis of component faults in the air-intake system of an automotive engine. The FDI scheme is first presented as a general methodology that permits the detection of faults in complex nonlinear systems without the need for building inverse models or numerous observers. Although sensor and actuator faults can be detected by this FDI methodology, component faults are generally more difficult to diagnose. Hence, this paper focuses on the detection and isolation of component faults for which the local approach is especially suitable. The challenge is to provide robust on-board diagnostics regardless of the inherent nonlinearities in a system and the random noise present. In particular, the local approach is shown to simplify and reduce the complicated FDI problem to the standard problem of detecting changes in the mean value of a Gaussian vector with a constant covariance matrix. In doing so, the least square score is implemented as our primary residual, and the statistical properties of the so-called improved residuals are used to detect changes in the parameters of a parametric model. Applying the local approach, in both offline and online configurations, to a mean-value model of a spark-ignition engine, component faults in the air-intake system, namely small abrupt changes in the volumetric efficiency coefficient <i>η<sub>o</sub></i> and the throttle discharge coefficient <i>C<sub>d</sub></i>, have been successfully detected and isolated. The FDI methodology shows robustness to modeling errors and noise, proving the fact that a perfect model is not required to monitor a given system. Since unknown plant parameters are usually determined by identification techniques, which never yield perfect results, modeling errors are indeed expected in practice. Consequently, the local approach sheds promises for robust on-board diagnostics in the face of the overall nonlinear powertrain problem.</div>

The experimental investigation on performance and emission characteristics of ceramic coated diesel engine using diesel and biodiesel

Linguistic OWA and two time-windows based fault identification in wide plants

Fault diagnosis is critical for safe and reliable operations of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce negative impacts of faults. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying model is established to describe quadrotor UAV dynamics, which considers measurement noises and external disturbances. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and H − / L ∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L ∞ performance condition, and actuator faults are detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residual vectors and defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains

This study presents a fault detection and isolation (FDI) method for open‐circuit faults (OCFs) in the switching devices of a grid‐connected neutral‐point‐clamped (NPC) inverter for photovoltaic (PV) applications. The proposed methodology addresses the fault diagnosis problem by a combined model‐based and data processing perspective to study single and simultaneous faults in the NPC inverter. For the model‐based scheme, a bank of sliding‐mode proportional–integral observers is suggested to estimate the fault profiles under an additive model. Thus, from the estimated fault profiles, and by performing a directional residual evaluation in a fixed reference frame, single and simultaneous fault scenarios can be isolated in the NPC inverter. However, for some fault classes, there is some ambiguity by just the model‐based approach that is overcome by employing the average line currents to construct extra fault signatures. The proposed FDI scheme only requires the measurements of line currents and grid voltages in the diagnosis media and can isolate single OCFs and simultaneous OCFs in the order or lower than a fundamental period of the grid frequency. Our new FDI methodology is validated through experimental data from a practical PV system in a closed‐loop grid‐connected NPC inverter under single and simultaneous OCF conditions.

The experimental stand is developed for registration of reflection and transparence coefficients in visible and IR diapasons, characteristic for radiant components of thermal fluxes for combustion chambers of diesel engines, turbine blades and a surface of aero-space apparatuses. Distinctive feature of the stand is a spectral photometric sphere for influence indemnification of scattering indicatrix (phase function). Estimations of scattering and absorption indexes of experimental and model samples of a semitransparent ceramic material on the base of stabilized zirconium dioxide for probing laser radiation at the fixed wavelengths 0,63 µm; 1,15 µm; 3,39 µm were carried out.