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Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Nowadays, the wavelet transformation and the 1-D wavelet technique provide valuable tools for signal processing, design, and analysis, in a wide range of control systems industrial applications, audio image and video compression, signal denoising, interpolation, image zooming, texture analysis, time-scale features extraction, multimedia, electrocardiogram signals analysis, and financial prediction.

Based on this awareness of the vast applicability of 1-D wavelet in signal processing applications as a feature extraction tool, this paper aims to take advantage of its ability to extract different patterns from signal data sets collected from healthy and faulty input-output signals. It is beneficial for developing various techniques, such as coding, signal processing denoising, filtering, reconstruction , prediction, diagnosis, detection and isolation of defects.

The proposed case study intends to extend the applicability of these techniques to detect the failures that occur in the battery management control system, such as sensor failures to measure the current, voltage and temperature inside an HEV rechargeable battery, as an alternative to Kalman filtering estimation techniques. Wavelet Theory. An essential internal parameter of the Li-ion battery is the state of charge SOC , defined as the available capacity of the cell that changes according to the current profile of the driving cycle.

Due to its crucial role in keeping the battery safe for various operating conditions and significantly extending battery life, SOC is a topic of great interest, as evidenced by an impressive number of research papers published in the literature. In the absence of a measurement sensor, the SOC must be estimated since its calculated value is not accurate enough.

The most used model-based Kalman filters can estimate the battery SOC with a high grade of accuracy [ 1 , 2 , 3 , 4 ]. The Li-ion battery is an important component integrated into battery management system BMS that performs tasks regarding the safe operation and reliability of the battery, protecting battery cells and battery systems against damage, as well as battery efficiency and service life [ 2 , 3 , 4 ].

A signal processing-based method using wavelet transforms proved to be a viable alternative to conventional Kalman filter state estimators, for designing and implementation of real-time FDI strategies. The new FDI approach avoids battery modeling difficulties and is more straightforward with better dynamic performance [ 7 ].

The drawback of this method is the difficulty experienced in dealing with the early faults and fault isolation. Its application also requires a large amount of calculations compared to the model-based methods. Similar, a wavelet-based transient fault detection and analysis is used successfully in [ 7 ] for a microgrid connected power. In this research, our motivation of using 1-D wavelet analysis comes from the preliminary results obtained for similar investigations on the impact of nonlinearities and uncertainties of actuators electro-pneumatic valves , such as hysteresis, dead zone, dead band, on a healthy pH neutralization plant [ 8 ].

An example of multisignal 1-D wavelet analysis is found in [ 9 ], and a useful tutorial of using wavelet transforms presented in [ 10 ]. A strong theoretical background on wavelet transform and their applications is provided by the fundamental work [ 13 ]. In [ 14 ] is presented an interesting fault isolation technique based on wavelet transform, and a detailed data-based FDI techniques for a nonlinear ship propulsion system are developed in [ 15 ].

Several multimedia applications of wavelet transform can be found in [ 16 ], and a better understanding of wavelet transform analysis, design and implementation of features extraction methods, for filtering, denoising, decomposition and reconstructing signals is given in [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Since of the lack of data in the literature field for similar situations developed in our research for Li-ion battery, it is not easy to make a state-of-art analysis of the results reported in the literature related to the FDI techniques design and implementation based on 1-D wavelet analysis.

The efficiency of 1-D analysis is proved in this paper based on extensive MATLAB simulations to extract the features of input-output signals such as the energy, skewness, kurtosis, and to compute the MSE statistical criteria performance. Finally, the MATLAB simulation results can provide useful information on detection accuracy, computation time, and robustness against measurement uncertainty, thus showing simply the effectiveness of the FDI proposed scheme.

The temperature fault is detected without doubt inside the Li-ion battery based on the significant values reached by the details D1, D2, and D3 and analysis coefficient A3 of the output terminal battery voltage residual level three decomposition, represented by the following sets of values 4. Also, the statistic RMSE performance criterion indicates significant values for D1 coefficient in the presence of the of temperature fault for energy feature 4. To detect both faults, a multiresolution analysis MRA is performed, capable of extracting a smooth trend term, which provides a valuable information to localize transient changes in the fault injection window [, ] seconds [ 23 ].

Thus, the presence of the bias current fault and bias temperature fault is detected and localized as a transient significant change in the nonstationary Li-ion output voltage residual signal. The fault signature and considering the variation trend in SOC residual and internal resistance of the battery also provides a piece of useful information for fault isolation. This section briefly presents the Rint equivalent circuit model Rint ECM as a case study to investigate the effectiveness of the proposed fault detection and isolation FDI strategy, using a conventional EKF SOC estimator, as a support for performance analysis comparison, in the first part [ 1 , 2 , 3 , 4 ], and a 1-D wavelet transformation in the second part [ 8 , 9 ].

For comparison purpose, an improved adaptive extended conventional Kalman AEKF filter algorithm [ 3 , 4 , 11 ] is also briefly presented for estimating the state of charge SOC of the adopted Li-ion battery, as well as the faults in Appendix A. Residual methodology is useful to detect and isolate faults. Only three failures of the current, voltage and temperature sensors of the HEV battery management system BMS used for the case study are analyzed.

The Rint ECM Li-ion battery model is one of the most common models to describe battery dynamics in many real-time implemented HEV applications with an acceptable range of performance. The reason for using these models is their simplicity, low number of parameters to adjust and easy implementation in a friendly MATLAB simulation environment. The main input-output and intermediate signals in Figure 1 are I batt is the input battery instantaneous value of the direct current DC flowing through the open circuit controlled-voltage source, and V batt denotes the measured output terminal battery instantaneous value DC voltage that are nonlinear dependent of OCV, as intermediate signal.

The internal resistance of the battery is affected by several factors. Still, a significant impact has conductor resistance, electrolyte resistance, ion mobility, separator efficiency, reactive electrode rates, polarization, temperature, and aging effects, and SOC changes, as is mentioned in [ 11 ].

Since the SOC of the battery is defined as [ 1 , 2 , 3 , 4 , 11 ]:. The battery terminal voltage Vbatt is related to OCV according to following nonlinear equation:.

However, for the implementation of the proposed FDI techniques, a high-precision model is not required, because the extraction of ECM parameters is beneficial to monitor the battery SOC, rather than to model the battery performance. Similar, the equivalent battery model in discrete time can be written in the following form:. Because the internal resistance Rin is an essential parameter of the battery that is affected much more by the temperature than other parameters of the cell, it is necessary to attach to the Li-ion battery model a thermal model, described in continuous time by a first order differential equation:.

In discrete time the Eq. The ECM Li-ion battery healthy model: a FTP driving cycle current profile; b output terminal voltage; ECM battery model SOC; d temperature profile for changes in ambient temperature; e the effect of temperature profile on battery internal resistance. For Li-ion batteries, the aspects such as accuracy performance of the SOC estimation and the prediction of the terminal voltage are essential to be analyzed, thus ensuring the safe operation of the cell, and thus maintaining a long life.

Therefore, a brief presentation of an appropriate estimation technique is of real use. Moreover, for any battery, whether it is a Li-ion battery, SOC cannot be measured accurately, so it is necessary to estimate it. Since the preliminary results obtained in [ 11 ] convinced us about the efficiency of applying the AEKF SOC estimator for a Simscape model of Li-ion battery, quite well documented in [ 4 ], then the same estimator is used in this paper.

For good documentation, the reader can see, in Appendix A, a brief presentation of the steps of AEKF estimation algorithm. Furthermore, the choice of using the AEKF for condition monitoring purposes is explained in this subsection. As is mentioned in the first section, the BMS, through its hardware and software components, plays a vital role in an HEV integrated structure for supervision, control and monitoring all the internal battery parameters.

In a BMS, time-based monitoring and FDI techniques based on Kalman filter state and parameters estimators are implementing, and the faults in a system are detected only when measured values exceeded their normal limits [ 5 , 26 ].

Figure 3 b depictures the residual battery terminal voltage calculated as a difference between the battery terminal voltage true values and the corresponding estimate values of battery terminal voltage, as in Eq.

The residues of battery SOC and for internal resistance are calculated by using the Eqs. For a healthy battery model, the residual is inside the minimum and maximum values of two thresholds, calculated as [ 5 ]:. A level of the noise in measurements is more realistic in HEVs applications since the initial value of SOC must be guessed, and due to contamination of the measurements with noise. The SOC residual that is showing in Figure 4 b remains inside the band delimited by the same minimum and maximum values of SOC threshold, and in Figure 4 c the battery terminal voltage residual also remains inside the band.

The fault injection mechanism based on AEKF fault estimation and residual generation consists of injecting additive bias sensors faults in the input-output Li-ion battery Rint ECM model, as following:. The Eq. As it can be seen in Eqs.

First scenario - bias sensor fault injection inside the window , seconds. In Figure 5 b is shown the impact of the injected fault on battery terminal voltage, real and estimated values. The MATLAB simulation result reveals an abnormal behavior of terminal voltage estimate inside the same window of fault injection. The residual battery terminal voltage is showing in Figure 5 c. It exceeds the band of the clean terminal voltage signal inside the fault window; thus, the same fault is detecting.

An abnormal behavior of battery SOC is revealed in Figure 5 d inside the fault window and persists inside the window until the fault is removed at instance The SOC residual generated by injecting the bias voltage in the Li-ion cell sensor terminal voltage is shown in Figure 5 e that also detects the occurrence of the fault inside the same window.

Second scenario: bias current sensor fault injection. Between samples and is injected a fault in the current measurement sensor of magnitude 2A, such is showing in the Figure B1 a from Annex B. Similar as for the first scenario the battery voltage reacts to the fault injection as is shown in Figure B1 b , and its residual depictured in Figure B1 c detects the presence of the fault at the beginning of the window injection.

In this scenario, compared to the first scenario, the fault persists until the end of the driving cycle; so its evolution after removing the fault is misclassified and can be considered as a false alarm, that is useful for constructing the FDI logic of fault localization isolation. In Figure B1 f the internal resistance Rin has the same evolution as in the first scenario. These last aspects are beneficial also for creating the FDI logic for isolation. Third scenario: injection of bias temperature sensor fault.

The battery terminal voltage is showing in Figure B2 f together to its residual depicted in Figure B2 g. This scenario of point view of fault detection is the same as the first scenario with the fault persistent only inside the window and removed at the end of the same window.

Only the internal resistance of the battery withstands a significant impact inside the window, a valuable indication for fault localization. More precisely, in a general formulation, the residue evaluation can be defined as:. For this purpose, a statistical evaluation function can be defined as [ 5 ]:. For the second scenario the isolation of the fault can be done based on the tendency of SOC, i. This section investigates the use, in a new approach, of 1-D wave signal analysis, a valuable tool for determining the essential characteristics of faults that occur in a Li-ion battery, a useful basic principle for developing a simple detection of their defects.

These techniques are based on detecting changes that occur abruptly in the variation of the residual signal due to a faulty current sensor or a defective temperature measurement sensor, such as those developed in the previous section. Therefore, a similar method of residual generation and evaluation is useful to provide a valuable information to use the wavelet transformation ability to extract the essential features patterns of the faults from the output voltage residual of the battery.

These faults visibly affect the performance of the Li-ion battery, such as the output voltage and SOC. The dynamics of the battery model under investigation is shown in Section 2.

Note that SOC plays a critical role in locating faults isolation. Over time, Fourier transform FT has proven to be a useful tool for analyzing signal frequency components in a wide variety of applications. However, it has a significant disadvantage, because when it covers the entire time axis, it is impossible to see when a frequency increase. Instead, the short-term Fourier transform STFT uses a sliding window to find the spectrogram, which provides complete information on both time and frequency.

A small impediment when using STFT in applications is due to the length of the window that limits the frequency resolution [ 10 ]. In these situations, the wavelet transforms WT seems to be a feasible solution, since it can be applied on a small wavelet of limited duration.

Specifically, the wavelet provides local frequency information compared to FT, which captures the global features such as the harmonic components of the entire signal. Besides, the scaled wavelets allow to analyze the signal on different scales. Thus, the wavelets form a complete basis, and the wavelet transforms are designed to be reversible. A wavelet is a waveform of effectively limited duration that has an average value of zero and nonzero norm, as is stated in [ 12 ]. Thus, a wavelet is a wave-like oscillation with an amplitude that starts at zero, increases, and then decreases back to zero.

A fundamental work recommended to readers to obtain an excellent theoretical background on the wavelets is the reference [ 13 ]. The CWT compares the signal under investigation, denoted by y t , to shifted and scaling compressed or stretched versions of the wavelet function [ 12 ].

Since the physical signal y t , which can be the output of the plant or a residual error, is real-valued, then also the CWT is a real-valued as a function of scale and position.

Software fault tolerance techniques and implementation pdf writer

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Automatic fault diagnosis of fault tolerant power converter for switched reluctance motor based on time-frequency technique Abstract: The concept of fault tolerance device and implementation of the automated algorithm based fault detection plays an important role in the reliable operation of rotating machinery. This is achieved by using a dual converter on a common power supply with six isolated phases.

The book examines key programming techniques such as. The book is intended for practitioners and researchers who are concerned with the dependability of software systems. Phases in the fault tolerance implementation of a fault tolerance technique depends on the design, configuration and application of a distributed system. Fault tolerance techniques are used to predict these failures and take an appropriate action before failures actually occur. Following are the methods for preventing programmers from introducing faulty code during development. We should accept that, relying on software techniques for obtaining dependability means accepting some overhead in terms of increased size of code and reduced performance or slower execution.

In this research, detection of failure in rolling element bearing faults by vibration Figure a A random spiky signal with Non-Gaussian p. Conventional bearing fault diagnosis methods require specialized instruments to acquire signals that can reflect This study proposes a new method for simplifying the instruments for motor bearing fault diagnosis. The motor is driven by a controller-fed BLDC driver. Induction machines are widely used in the industry as one of the major actuators, such as water pumps, air compressors, and fans. It is necessary to monitor and diagnose these induction motors to prevent any sudden shut downs caused by premature failures. Numerous fault detection and isolation techniques for the diagnosis of induction machines have been proposed over the past few decades.


Download full-text PDF · Read full-text · Download The classifier is evaluated in fault diagnosis of a DC drive system. Fault simulation: field converter short-​circuit. +10 Clustering Algorithm and Drift Detection. Maurilio.


Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes

Many investigators are interested in improving the control strategies of hand prosthesis to make it functional and more convenient to use. However, these biological signals are very sensitive to many disturbances and are generally unpredictable in time, type, and level. This leads to inaccurate identification of user intent and threatens the prosthesis control reliability. This paper proposed a real-time fault detection and localization approach applied to handwriting device on the plane.

Rolling element bearings play vital role in the working of rotating hardware or machine. The imperfection-initiated vibration signal estimation and its examination is frequently utilized in deficiency recognition of direction. The crude sign is mind boggling in nature to dissect for deformity highlights, Therefore the sign be prepared to break down it. This article presents different sign handling procedures including canny strategies, for example, Artificial Techniques, Machine learning techniques and so on. The suitability of these strategies, all things considered, depends on the idea of features isolated from the bearing signs.

Nowadays, the wavelet transformation and the 1-D wavelet technique provide valuable tools for signal processing, design, and analysis, in a wide range of control systems industrial applications, audio image and video compression, signal denoising, interpolation, image zooming, texture analysis, time-scale features extraction, multimedia, electrocardiogram signals analysis, and financial prediction. Based on this awareness of the vast applicability of 1-D wavelet in signal processing applications as a feature extraction tool, this paper aims to take advantage of its ability to extract different patterns from signal data sets collected from healthy and faulty input-output signals. It is beneficial for developing various techniques, such as coding, signal processing denoising, filtering, reconstruction , prediction, diagnosis, detection and isolation of defects. The proposed case study intends to extend the applicability of these techniques to detect the failures that occur in the battery management control system, such as sensor failures to measure the current, voltage and temperature inside an HEV rechargeable battery, as an alternative to Kalman filtering estimation techniques.

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Building fault detection data to aid diagnostic algorithm creation and performance testing

Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory LSTM network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults.

A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

It seems that you're in Germany. We have a dedicated site for Germany. Authors: Russell , Evan L. Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data.


Complexity. mydowntownsmyrna.org In fact, nowadays, Fault Detection and Diagnosis (​FDD) to the writers, the nature of the produced form, and even.


A Review on Intelligent Fault Detection in Rolling Element Bearings

В XVI11 веке некий английский купец приобрел у севильской церкви три десятка бушелей апельсинов и, привезя их в Лондон, обнаружил, что фрукты горькие и несъедобные. Он попытался сделать из апельсиновой кожуры джем, но чтобы можно было взять его в рот, в него пришлось добавить огромное количество сахара. Так появился апельсиновый мармелад. Халохот пробирался между деревьями с пистолетом в руке. Деревья были очень старыми, с высокими голыми стволами. Даже до нижних веток было не достать, а за неширокими стволами невозможно спрятаться. Халохот быстро убедился, что сад пуст, и поднял глаза вверх, на Гиральду.

Теперь он уже бежал по узкому проходу. Шаги все приближались. Беккер оказался на прямом отрезке, когда вдруг улочка начала подниматься вверх, становясь все круче и круче.

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