You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches 
Chapter Title:  A. Time-Domain Signal-Based Methods 
1. Abstract of Cited Paper (BIB001):  In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches. 
2. Abstract of Cited Paper (BIB002):  This paper describes four main fault types of the asymmetric bridge power converter in switched reluctance motor drive on power transistors. Two on-line fault diagnosis methods for power transistors in the power converter are proposed. The principle of the proposed diagnosis methods is to detect the real-time current state from some particular positions, and then obtain the diagnosis result and the fault location by logical judgment. One fault diagnosis method is proposed using single current sensor monitoring the chopped bus current; the other method is using dual current sensors scheme monitoring the upper freewheeling bus current and excitation bus current. The simulation results of current states from certain positions of a three-phase 12/8 motor and its power converter are analyzed. Experiments have verified the effectiveness of the proposed fault diagnosis methods. It is shown that the dual current sensors scheme monitoring the upper freewheeling bus current and excitation bus current has the fastest fault response. 
3. Abstract of Cited Paper (BIB003):  Condition monitoring and fault diagnosis are currently considered crucial means to increase the reliability and availability of wind turbines and, consequently, to reduce the wind energy cost. With similar goals, direct-drive wind turbines based on permanent magnet synchronous generators (PMSGs) with full-scale power converters are an emerging and promising technology. Numerous studies show that power converters are a significant contributor to the overall failure rate of modern wind turbines. In this context, open-circuit fault diagnosis in the two power converters of a PMSG drive for wind turbine applications is addressed in this paper. A diagnostic method is proposed for each power converter, allowing real-time detection and localization of multiple open-circuit faults. The proposed methods are suitable for integration into the drive controller and triggering remedial actions. In order to prove the reliability and effectiveness of the proposed fault diagnostic methods, several simulation and experimental results are presented. 
4. Abstract of Cited Paper (BIB004):  Fault detection (FD) in power electronic converters is necessary in embedded and safety critical applications to prevent further damage. Fast FD is a mandatory step in order to make a suitable response to a fault in one of the semiconductor devices. The aim of this study is to present a fast yet robust method for fault diagnosis in nonisolated dc–dc converters. FD is based on time and current criteria which observe the slope of the inductor current over the time. It is realized by using a hybrid structure via coordinated operation of two FD subsystems that work in parallel. No additional sensors, which increase system cost and reduce reliability, are required for this detection method. For validation, computer simulations are first carried out. The proposed detection scheme is validated on a boost converter. Effects of input disturbances and the closed-loop control are also considered. In the experimental setup, a field programmable gate array digital target is used for the implementation of the proposed method, to perform a very fast switch FD. Results show that, with the presented method, FD is robust and can be done in a few microseconds. 
5. Abstract of Cited Paper (BIB005):  Fault diagnosis of induction motors in the practical industrial fields is always a challenging task due to the difficulty that lies in exact identification of fault signatures at various motor operating conditions in the presence of background noise produced by other mechanical subsystems. Several signal processing approaches have been adopted so far to mitigate the effect of this background noise in the acquired sensor signal so that fault-related features can be extracted effectively. Addressing this issue, this paper proposes a new approach for fault diagnosis of induction motors utilizing two-dimensional texture analysis based on local binary patterns (LBPs). Firstly, time domain vibration signals acquired from the operating motor are converted into two-dimensional gray-scale images. Then, discriminating texture features are extracted from these images employing LBP operator. These local feature descriptors are later utilized by multi-class support vector machine to identify faults of induction motors. The efficient texture analysis capability as well as the gray-scale invariance property of the LBP operators enables the proposed system to achieve impressive diagnostic performance even in the presence of high background noise. Comparative analysis reveals that LBP8,1 is the most suitable texture analysis operator for the proposed system due to its perfect classification performance along with the lowest degree of computational complexity. 
6. Abstract of Cited Paper (BIB006):  Abstract Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.

Literature Review:
For a continuous dynamical process to be monitored, it is natural to extract time-domain features for fault diagnosis. For instance, in BIB003 , by analyzing the changes of the measured root-mean-square current characteristics between healthy conditions and the situations under single-/dual-transistor short circuit or open circuit, a fault diagnosis method was developed for power converters of switched reluctance motors. In BIB004 , the absolute value of the derivative of the Park's vector phase angle was used as a fault indicator, which was employed for diagnosing multiple open-circuit faults in two converters of permanent-magnet synchronous generator drives for wind turbine applications. By observing the slope of the induction current over time, a fault diagnosis method was addressed in BIB005 for open and short circuits switch fault diagnosis in nonisolated dc-dc converters, and the field-programmable gate array digital target was then used for real-time experimental implementation. In  , by using the measured motor phase currents and their corresponding reference signals, a real-time algorithm was developed for detecting and locating multiple power switch open-circuit faults in inverted-fed ac motor drives. In  , it was shown that, under balanced supply voltage, the phase angle and the magnitude of the negative and zerosequence currents can be considered as reliable indicators of stator faults in the induction motors. In BIB001 , a statistical method for the detection of sensor abrupt faults in aircraft control systems was presented, where the covariance of the sensing signals was used for feature extraction. Recently, a time-domain signal-based diagnostic algorithm has been developed in BIB007 for monitoring of gear faults, by combining fast dynamic time warping (fast DTW) and correlated kurtosis (CK) techniques. The fast DTW algorithm was employed to extract the periodic impulse excitations caused from the faulty gear tooth, and the extracted signal was then resampled for subsequent diagnostic analysis using the CK technique. Taking advantages of the periodicity of the geared faults, the CK algorithm can identify the position of the local gear fault in the gearbox. Different from the approaches for fault detection and diagnosis using features of the measured signal in one-dimensional domain, a two-dimensional signal-based method was proposed in BIB002 , where the vibration signal was translated into an image (two dimensions), and the local features were then extracted from the image using scale-invariant feature transform (SIFT) for fault detection and isolation under a pattern classification framework. Very recently, a two-dimensional approach has been reported in BIB006 for fault diagnosis of induction motors, where time-domain vibration signals acquired from the operating motor were first converted into two-dimensional gray-scale images, and the discriminating texture features were then extracted from these images utilizing local binary patterns (LBP) technique. The extracted texture features were finally used for fault diagnosis with the aid of a classifier. It is noted that, when converting signals into images, the added noise acts as illumination variation. As both the SIFT technique and the LBP operator have illumination invariance capability to some extent, the proposed fault diagnosis methods in BIB002 and BIB006 have robustness even in a high level of background noises.