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This paper proposes a new combination of correlative statistical analysis and the sliding window technique to detect incipient faults. Compared with the existing monitoring methods based on principal component analysis and transformed component analysis, the combination fully uses the information from the process and quality variables. The sliding window, however, inevitably increases the computation burden due to the repeated window calculations. Therefore, a recursive algorithm is proposed in this paper, which has been shown to have less calculation complexity. Furthermore, a randomized algorithm is proposed to determine the width of sliding window. A numerical example and the thermal power plant process are presented to show the effectiveness and advantages of the proposed method.

Few studies have specifically evaluated the comminution extent of lateral femoral wall (LFW) fracture and risk factors of implant failure in intertrochanteric fractures with LFW fracture. The aim of present study was to evaluate the influence of comminution extent of LFW fracture on implant failure and identify risk factors of implant failure in cases with LFW fracture after intramedullary fixation.This retrospective study included 130 intertrochanteric fracture with LFW fracture treated with intramedullary fixation at a teaching hospital over a 13-year period from January 2006 to December 2018. Demographic information, cortical thickness index, the reduction quality, status of medial support, position of the screw/blade and status of lateral femoral wall were collected and compared. The logistic regression analyzes was performed to evaluate risk factors of implant failure in intertrochanteric fractures with LFW fracture after intramedullary nail fixation.10 patients (7.69%) suffered from mechanical failure after intramedullary fixation. Univariate analyzes showed that comminuted LFW fracture (OR, 7.625; 95%CI, 1.437~40.446; p = 0.017), poor reduction quality (OR, 49.375; 95%CI, 7.217~337.804; p 0.001) and loss of medial support (OR, 17.818; 95%CI, 3.537~89.768; p 0.001) were associated with implant failure. After adjustment for confounding variables, the multivariable logistic regression analyzes showed that poor reduction quality (OR, 11.318; 95%CI, 1.126~113.755; p = 0.039) and loss of medial support (OR, 7.734; 95%CI, 1.062~56.327; p = 0.043) were independent risk factors for implant failure. Whereas, comminuted LFW fracture was not associated with implant failure (p = 0.429).The comminution extent of the LFW fracture might influence the stability of intertrochanteric fractures; and intramedullary fixation might be an effective treatment method. Furthermore, poor reduction quality and loss of medial support could increaze the risk of implant failure in intertrochanteric fractures with LFW fractures after intramedullary fixation. Therefore, we should pay great emphasis on fracture reduction quality in future.

For industrial processes, one common drawback of conventional process monitoring methods is that they would make an increasing number of false alarms in cases of various factors such as catalyst deactivation, seasonal fluctuation and so forth. To address this issue, the present work proposes an online dictionary learning method, which can fulfill the process monitoring and fault diagnosis task adaptively. The proposed method would incorporate currently available information to update the dictionary and control limit, instead of keeping a fixed monitoring model. The online dictionary learning method are more superior than conventional methods. Firstly, compared with some traditional offline methods based on small amounts of historical data, the proposed method can augment train data with online dictionary updating, thus it copes with time-varying processes well. Secondly, the proposed method enjoys a lower computational complexity than the offline learning method with mass data, which is appealing in the era of industrial big data. Thirdly, the proposed method performs more reliably than the existing recursive principal component analysis-based methods because it can resolve the anomaly of principal component or non-orthogonality of eigenvectors problem which was often confronted in the recursive principal component analysis-based methods. Finally, some experiments were designed and carried out to demonstrate the advantage of the online dictionary learning.

For industrial processes, there are usually a number of measurement sensors equipped for monitoring and control purposes. In practice, sensors may suffer from the precision degradation phenomenon due to several aspects such as aging and ambient interference. This phenomenon may lead to imprecise or even incorrect control commands and indications, so the corresponding fault detection task is of vital importance. In this paper, inspired by the fact that precision degradation of a sensor can result in the increase of the variable’s variance, an algorithm based on second-order statistics analysis is proposed to accomplish the detection task for sensor precision degradation faults. By employing the sliding window technique, second-order statistics of process variables are first extracted. Then, conventional principal component analysis (PCA) is used as a dissimilarity quantification tool, with detection statistics and corresponding control limits established, to perform fault detection. Finally, simulations on a numerical example and the continuous stirred tank reactor (CSTR) benchmark process are performed to illustrate the effectiveness and advantages of the proposed method, in comparison with some existing methods such as PCA, dynamic PCA, and dissimilarity (DISSIM).

OBJECTIVE To assess the therapeutic effect of a novel tension band using 3.0 mm cannulated screw combined with a titanium cable and specific shims comparatively with patellotibial tubercle cerclage in comminuted fractures of the inferior pole of the patella. METHODS The retrospective study from March 2012 to July 2017 was conducted in Peking University Third Hospital and comprised 63 patients with comminuted fractures of the inferior pole of the patella: 41 treated with new tension band using 3.0 mm cannulated screw combined with a titanium cable and specific shims (new tension band group) and 22 with patellotibial tubercle cerclage (tubercle cerclage group). Gender, age, AO/OTA fracture type, injury mechanism, inter-fragmentary gap, and follow-up time of patients were recorded. Two groups were compared regarding: operation time, blood loss, partial weight-bearing time, fracture-healed time, Bostman score and knee mobility at 12-month follow-up, and postoperative complications. Continuous and categorical parameters were analyzed by Mann-Whitney U test and the chi-squared test, respectively. Fisher's exact test was used for small data subsets. RESULTS Between the two groups, no statistically significant difference was found in mean age, gender, AO/OTA fracture type, injury mechanism, mean inter-fragmentary gap, or mean follow-up time (P > 0.05). The mean operation time of new tension band group was significantly longer than that of tubercle cerclage group (76.4 min vs 64.2 min, P = 0.006), while there was no significant difference in blood loss. After surgery, new tension band group had a significantly earlier mean partial weight-bearing time (5.2 weeks vs 7.4 weeks, P

ObjectiveTo compare the clinical outcomes between use of sliding fixation (three cannulated screws, TCS) and non-sliding fixation (four cannulated screws, FCS) in the treatment of femoral neck fractures.MethodsWe retrospectively analyzed 102 patients with fresh femoral neck fractures treated with TCS (60 cases) and FCS (42 cases) between January, 2018 and December, 2019. The demographic data, follow-up time, hospitalization time, operation time, blood loss, length of femoral neck shortening (LFNS), soft tissue irritation of the thigh (STIT), Harris hip score, and complications (such as internal fixation failure, non-union, and avascular necrosis of the femoral head) were also collected, recorded, and compared between the two groups.ResultsA total of 102 patients with an average age of 60.9 (range, 18–86) years were analyzed. The median follow-up time was 25 (22 to 32) months. The LFNS in the FCS group (median 1.2 mm) was significantly lower than that in the TCS group (median 2.8 mm) (P < 0.05). In the Garden classification, the number of displaced fractures in the TCS group was significantly lower than that in the FCS group (P < 0.05). The median hospitalization time, operation time, blood loss, reduction quality, internal fixation failure rate (IFFR), STIT, and Harris hip score were not statistically different between the two groups (P > 0.05). However, in the subgroup analysis of displaced fractures, the LFNS (median 1.2 mm), STIT (2/22, 13.6%), and Harris hip score (median 91.5) of the FCS group at the last follow-up were significantly better than the LFNS (median 5.7 mm), STIT (7/16, 43.8%), and Harris hip score (median 89) of the TCS group (P < 0.05). No complications such as incision infection, deep infection, pulmonary embolism, or femoral head necrosis were found in either group.ConclusionTCS and FCS are effective for treating femoral neck fractures. For non-displaced fractures, there was no significant difference in the clinical outcomes between the two groups. However, for displaced fractures, the LFNS of the FCS is significantly lower than that of the TCS, which may reduce the occurrence of STIT and improve the Harris hip score.

To compare the short-term outcomes of the femoral neck system (FNS) with multiple cancellous screws (MCS) and dynamic hip screws (DHS) in the treatment of femoral neck fractures.A retrospective analysis was performed on 157 patients with fresh femoral neck fractures treated with FNS (from September 2019 to October 2020), MCS, and DHS (from January 2018 to October 2020). According to internal fixation methods, all patients were divided into the FNS group (54 cases), MCS group (51 cases), and DHS group (52 cases). Demographic data were also collected, recorded, and compared, including the follow-up time, days of hospitalization, operation time, blood loss, intraoperative fluoroscopy times (IFT), weight-bearing time (WBT), length of femoral neck shortening (LFNS), Harris hip score, and complications (such as internal fixation failure, bone nonunion, and avascular necrosis of the femoral head) between the three groups.Overall, 157 patients with a mean age of 61.8 (range, 18-89) years were analyzed. In the FNS group, IFT (median 10.5) was significantly lower than that in the MCS group (median 21) and DHS group (median 20.5) (P0.05), and WBT was significantly earlier than that of the MCS group (P0.05). In the FNS group, the median hospitalization time, operation time, and blood loss were 2 (2, 4) days, 45 (40-59) min, and 30 (20, 50) ml, respectively. They were all significantly less than 3.5 (3, 6) days, 72 (55-88.75) min, and 50 (30, 50) ml in the DHS group, respectively (P 0.05). There was no statistical difference in LFNS, quality of reduction, internal fixation failure rate, and Harris hip score at the latest follow-up (P0.05). No surgical complications such as incision infection, deep infection, pulmonary embolism, or femoral head necrosis were found in any of the three groups.FNS, MCS, and DHS are all effective for femoral neck fractures. However, a lower IFT of FNS shortens the operation time. Compared to MCS, FNS has the advantages of being a simple operation, with early WBT; compared to DHS, a minimally invasive operation is the main advantage of FNS.

In practice, because of complex mechanism processes, such as heating process, volume heterogeneity, and various chemical reaction characteristics, there is a nonlinear relationship among variables in industrial systems. The nonlinearity brings some difficulties to process monitoring. In order to ensure that the process monitoring system can work normally in nonlinear production processes, the nonlinear relationship between variables ought to be considered. In this work, a new fault detection and isolation method based on kernel dictionary learning is presented. In detail, the linearly inseparable data is mapped to a high-dimensional space. Then, a new nonlinear dictionary learning method based on kernel method was proposed to learn the dictionary. After obtaining the dictionary, the control limit can be calculated from the training data according to the kernel density estimation (KDE) method. When new data arrive, they can be represented by the well-learned dictionary, and the kernel reconstruction error can be used as a classifier for process monitoring. As for the fault data, the iterative reconstruction based method is proposed for fault isolation. In order to evaluate the effectiveness of the proposed process monitoring method, some extensive experiments on a numerical simulation, the continuous stirred tank heater (CSTH) process, and a real industrial aluminum electrolysis process are conducted. The proposed method is compared with several state-of-the-art process monitoring methods and the experimental results show that the proposed method can provide satisfactory monitoring results, especially for some small faults, thus it is suitable for process monitoring of nonlinear industrial processes.

Incipient sensor fault diagnosis is important to an efficient and optimal operating condition for modern industrial systems. Recently, a new fault detection index called augmented Mahalanobis distance (AMD) has been proposed in our previous work for incipient fault detection. Following detection, fault isolation is also quite desired so as to investigate root causes of the occurred fault. In the present work, the AMD statistic is first revisited and a geometric illustration of AMD is provided, which intuitively shows its superiority for incipient fault detection. Then, with available fault direction information, an incipient sensor fault isolation approach is proposed. Its fault isolability condition is analyzed theoretically and compared with that of the conventional method. For complex sensor faults whose fault direction information is unknown, a corresponding fault isolation strategy is also briefly discussed. Case studies on a high-speed train air brake system and the continuous stirred tank reactor (CSTR) process are carried out, which demonstrate the effectiveness of the AMD based fault detection and isolation methods, in comparison with conventional approaches.

This paper considers the problem of detecting and isolating incipient sensor fault in multimode processes. A data-driven multimode process monitoring method called conditionally independent Bayesian learning based recursive transformed component statistical analysis (CIBL-RTCSA) is presented. Considering the strong assumption of conditional independence in naive Bayes, orthogonal transformation is applied to measured variables to improve the extent of conditional independence in different operating modes. The Bayes-based mode identification is adopted for transformed data, and a multiple RTCSA model with a window-switching scheme is developed for monitoring multimode processes. With the orthogonal transformation, the accuracy of mode identification can be effectively improved compared with naive Bayes. In addition, the fault detection and isolation performance of the proposed method outperforms traditional monitoring methods. The effectiveness of the proposed method is demonstrated by a numerical example and the simulation on a continuous stirred tank heater process.

In order to solve the problem of low position accuracy due to weak traveling wave signal in the case of high impendence earth fault in the distribution network, a method based on variational modal decomposition (VMD) and wavelet max modulus(WMM) method to realize the fault location under weak traveling waves. Firstly, using VMD on the component of fault current line to get multiple intrinsic mode function (IMF). Then, the mutation characteristics of the first IMF is received with the help of WMM to determine the arriving time of the weak traveling wavefront. Finally, PSCAD/EMTDC simulation results verify correctness and effectiveness of the proposed method.

Background Limited studies were available to investigate the prevalence of deep vein thrombosis (DVT) on admission in elderly patients with intertrochanteric fractures. The aim of present study was to evaluate risk factors and the prevalence of pre-admission DVT in elderly patients with intertrochanteric fractures. Methods This retrospective study included 788 elderly patients with intertrochanteric fracture who were eligible for this study from January 1, 2010, to December 31, 2019. Color doppler ultrasonography was performed for DVT detection at admission. All patients’ clinical data were collected. Univariate analysis and stepwise backward multivariate logistic regression were used to identify the risk factors contributing to the occurrence of DVT. Results The overall prevalence of pre-admission DVT in patients with intertrochanteric fractures was 20.81% (164 of 788 patients). The mean time from injury to admission was 2.1 days in the total population, 2.96 and 1.87 days in patients with and without DVT. Univariate analysis showed that significantly elevated risk of DVT were found in patients with longer time from injury to admission, high energy injury, lower Hb value, higher BMI, diabetes, chronic obstructive pulmonary disease (COPD), atrial fibrillation, dementia, varicose veins, higher age-adjusted CCI, higher ASA class and A3 type intertrochanteric fractures (P Conclusions A high prevalence of pre-admission DVT was found in elderly Chinese patients with intertrochanteric fractures. Therefore, surgeons should be aware of the high prevalence of DVT for elderly patients with intertrochanteric fractures in order to prevent intraoperative and postoperative PE and other lethal complications.

Background We aimed to distinguish the preoperative radiological indicators to predict the application of assistant techniques during intubation for patients undergoing selective cervical surgery. Methods A total of 104 patients were enrolled in this study. According to whether intubation was successfully accomplished by simple Macintosh laryngoscopy, patients were divided into Macintosh laryngoscopy group (n = 78) and Assistant technique group (n = 26). We measured patients’ radiographical data via their preoperative X-ray and MRI images, and compared the differences between two groups. Binary logistic regression model was applied to distinguish the meaningful predictors. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to describe the discrimination ability of indicators. The highest Youden’s index corresponded to an optimal cut-off value. Results Ten variables exhibited significant statistical differences between two groups (P Conclusions Four radiological variables possessed potential ability to predict the application of assistant intubation techniques. Anaesthesiologists are recommended to apply assistant techniques more positively once encountering the mentioned cut-off values.

Background Due to various reasons, the diagnosis of cervical spine fracture (CSF) in patients with ankylosing spondylitis (AS) may be missed or delayed in clinical practice.Objective To explore the prediction ability of simple plain X-ray for the possibility of CSF in AS patients who suffer from low-energy trauma (LET).Methods From January 2010 to December 2019, AS patients who experienced LET were retrospectively reviewed. Their clinical data, including gender, age, body mass index, time interval between AS diagnosis and trauma, smoking or not, and a presence of continuous bony bridge between anterior margin of C1 and C2 body or not was collected. Besides, distances and angles were measured by the lateral cervical X-ray.Results 129 AS patients were divided into Fracture group (41 cases) and Non-fracture group (88 cases) based on whether CSF existed. A total of twelve parameters had significantly statistical differences between two groups (P P P = 0.031, OR = 1.057), Borden’s index (P = 0.042, OR = 1.131), the time interval between the AS diagnosis and the trauma (P Limitations The number of the included patients in the present study was relatively small.Conclusions Four parameters measured by simple X-ray show prediction ability for the occurrence of CSF in AS patients who encounter LET.

Study design A retrospective single-center study. Objective We aimed to compare the clinical outcomes of cervical spine fracture accompanied with ankylosing spondylitis (ASCSF) treated by single posterior approach (PA) and combined anterior-posterior approach (CA) for patients who were followed up for >1 year. Summary of background data For ASCSF patients, surgical treatment has been widely accepted as a recommendable therapeutic option. But the optimal surgical approach is still under controversy, and few studies have focused on the comparison between PA and CA. Materials and methods From February 2007 to March 2019, 53 patients were enrolled and divided into the PA group (34 cases) and CA group (19 cases). Their general characteristics and clinical materials were recorded. From the aspects of reduction distance, bone fusion, neurological functional restoration, and postoperative complications, patients' surgical outcomes were evaluated qualitatively and quantitatively. Results The reduction degree of dislocation (mean PA=2.05 mm, mean CA=2.36 mm, P=0.94) was close between the 2 groups. Besides, with a similar follow-up period (P=0.10), the rate of bone fusion (both 100%) and neurological functional restoration (PA=31.03%, CA=35.29%, P=0.77) were also without significant difference. The occurrence rate of postoperative complications tended to be higher in the CA group (31.58% vs. 23.53%) but with no significant difference (P=0.52). Nevertheless, the surgical duration time (mean=209.15 min) and blood loss (average=388.91 mL) of PA group were significantly less than CA group (mean duration time=285.34 min, mean blood loss=579.27 mL) (P Conclusions Compared with to the CA approach and with the equally significant outcome, surgery by single PA was feasible and should be positively recommended for ASCSF patients, especially for those accompanying with a severe chin-on-chest deformity or poor physical conditions which restrain patients from tolerating a long surgery or major surgical trauma.

Five patients with segmental irregular-shaped bone defect of the femur were recruited in this study from 2017.12 to 2018.11. All patients were treated by customized design and 3D printed micro-porous prosthesis. And the procedure was divided into stages: radical debridement and temporary fixation (the first stage); the membrane formation and virtual surgery (intervening period for 6-8 weeks); definite reconstruction the defects (the second stage). Routine clinical follow-up and radiographic evaluation were done to assess bone incorporation and complications of internal fixation. The weight-bearing time and the joint function of the patients were recorded. The patients were followed up for an average of 16.4 months. The average length of bone defect and the distal residual bone was 12 cm and 6.5 cm. The average time of partial weight-bearing and full weight-bearing was 12.7 days and 2.6 months. X-ray demonstrated good osseous integration of the implant/bone interface. No complications occurred such as implant loosening, subsidence, loss of correction and infection. At the last follow-up, Harris score of hip joint was excellent in 2 cases, good in 2 cases, fair in 1 case; HSS score of knee joint was good in 4 cases, middle in 1 case. From our study, we concluded that meticulous customized design 3D printed micro-porous prosthesis combined with intramedullary nail may be a promising and an alternative strategy to treat metaphyseal segmental irregular-shaped femoral bone defect, especially for cases with massive juxta-articular bone loss.

Fault diagnosis plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). In this study, a hybrid feature model and deep learning based fault diagnosis for UAV sensors is proposed. The residual signals of different sensor faults, including global positioning system (GPS), inertial measurement unit (IMU), air data system (ADS), were collected. This paper used short time fourier transform (STFT) to transform the residual signal to the corresponding time-frequency map. Then, a convolutional neural network (CNN) was used to extract the feature of the map and the fault diagnosis of the UAV sensors was implemented. Finally, the performance of the proposed methodology is evaluated through flight experiments of the UAV. From the visualization, the sensor faults information can be extracted by CNN and the fault diagnosis logic between the residuals and the health status can be constructed successfully.

Air brake systems are crucial systems for safe and stable operation of electric multiple units (EMUs). The brake cylinder system, which includes brake cylinders, corresponding pressure sensors, and connection pipes, plays a vital role in the EMU air brake system. This is because brake cylinder pressures directly affect the brake operation. Currently, brake cylinder pressures are monitored by univariate control charts, i.e., the brake cylinder system will be considered as faulty if a certain pressure goes beyond its allowed range. Besides, serious sensor hardware faults such as open circuit or short circuit can also be detected by system self-inspection circuits. However, three kinds of faults, including brake cylinder component fault, soft sensor fault, as well as gas leakage fault, cannot be well handled by current monitoring methods if these faults are not very serious. In this paper, a fault detection index called intervariable variance (IVV) is first presented to perform fault detection for these faults. Fault detectability analysis is provided, and the IVV statistic is also compared with the univariate control chart approach. Then, a fault isolation strategy is proposed to distinguish different kinds of faults and determine the location of the occurred fault. Finally, the effectiveness of the proposed fault detection and isolation method is demonstrated via extensive experimental studies that are carried out on the EMU brake test bench of Qingdao Sifang Rolling Stock Research Institute Co., Ltd., China.

Substation equipments are faced with incomplete information, multiple system data cannot be effectively shared, and maintenance personnel are not aware of the status of the equipment. In view of the above problems, this paper proposes a substation intelligent monitoring decision platform based on panorama data, which can realize the centralized access of substation main equipment and auxiliary equipment datas. The equipment state assessment model and intelligent load transfer decision-making base are established based on the decision platform, which realize the intelligent judgment of quantifying equipment fault and abnormal state by comprehensive evaluation method, and provide transfer strategy for substation based on device status discrimination result. Finally, The transfer strategy and model are validated by an actual engineering data, the results show that based on the method proposed in this paper, all optional load transfer schemes are formed and the best solution is given, which can improve the emergency repair and pre-control ability and have great practical value in the automation system of substation and deserves extensive application.

Detecting early abnormalities in blast furnaces is important for the smooth operation of the iron-making process. In this paper, recursive transformed component statistical analysis (RTCSA)-based algorithms are developed to monitor the iron-making process with the task of early abnormality detection. The increments of variables instead of the absolute measurements are used for RTCSA, in order to decrease the effect of the time-varying nature of the process. Owing to the peak-like disturbances caused by the switching of hot blast stoves, an online identification algorithm is designed to locate the disturbance intervals. Then an index-switching scheme is used for monitoring the process. The effectiveness of the proposed method is verified using the real data of two blast furnaces. Compared with the conventional methods such as the two-stage principal component analysis, the increment-based RTCSA can effectively detect early abnormalities in the iron-making process.

Accurate detection and isolation of an incipient fault is crucial for efficient and optimal operation of industrial processes. In the area of statistical process monitoring (SPM), reconstruction-based methods are widely used for fault isolation purposes. Though possessing several merits, most existing conventional reconstruction methods are not efficient in isolating incipient faults that have small magnitudes. This paper proposes a new method called exponential smoothing reconstruction (ESR) for incipient fault isolation. The proposed ESR introduces the exponentially weighted moving average (EWMA) technique to conventional reconstruction methods. Fault isolability analysis of the ESR is provided, which demonstrates its efficiency in isolating incipient faults, compared with conventional reconstruction-based methods. Several remarks on the use of ESR, such as selection of the smoothing parameter and some practical issues, are discussed as well. Finally, simulation studies on a numerical example and a cont...

This paper considers the isolation problem of incipient sensor fault. Based on recursive transformed component statistical analysis (RTCSA), two different isolation methods are proposed. The first method is called subspace reconstruction, where elements in specific subspaces are eliminated, and then reconstructed by minimizing the reconstructed detection index. The faulty variable is determined by the least scaled reconstructed detection index. The second method is called subblock detection, which has less online computational complexity. The subblocks of the measurement matrix are sequentially selected in each sliding window to calculate the subblock detection indices, and the faulty variable is determined by the largest subblock detection margin. Compared with the existing isolation methods such as reconstruction-based contribution (RBC) and its variant termed as average residual-difference reconstruction contribution plot (ARdR-CP), the superior isolation performances of the proposed methods are illustrated by a numerical example as well as a simulation on a continuous stirred tank reactor.