Search

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).

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.

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.

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.

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.

To investigate potential predictors of implant failure following fixation of proximal femoral fractures with a fracture of the lateral femoral wall.Medical records of 99 adult patients who had operative treatment for a proximal femoral fracture with a fracture of the lateral femoral wall between May 2004 and April 2015 were retrospectively analysed to determine factors associated with implant failure. Patients underwent routine surgical procedures for implantation of extramedullary or intramedullary devices. Potential predictors were age, gender, body mass index, comorbidities, type of fracture, reduction method, status of greater and lesser trochanters, course of the lateral fracture line, and presence/absence of a free bone fragment at the junction of the greater trochanter and lateral femoral wall.Ten (10%) implant failures were identified. Univariate analysis identified a free bone fragment at the junction of the greater trochanter and lateral femoral wall (odds ratio [OR], 21.25; 95% confidence interval [CI], 4.31-104.67; p 0.001) and a transverse fracture line across the lateral femoral wall (primary or iatrogenic) (OR, 5.36; 95% CI, 1.29-22.30; p = 0.021) as factors associated with implant failure. Using a multivariate model, only a free bone fragment at the junction of the greater trochanter and lateral femoral wall (OR, 16.05; 95% CI, 3.06-84.23; p = 0.001) was a risk factor for implant failure.A free bone fragment at the junction of the greater trochanter and lateral femoral wall and a transverse fracture line across the lateral femoral wall are predictors of implant failure in proximal femoral fractures with a fracture of the lateral femoral wall. Integrity of the lateral femoral wall correlates with prognosis of proximal femoral fracture. Lateral femoral wall reconstruction may be required for effective treatment of proximal femoral fractures with a fracture of the lateral femoral wall.

Planetary gearbox is one of the most important components of rotating machinery and plays a key role in modern industry. Due to the complex physical structures and harsh working conditions, planetary gearbox often suffers from different fault types, so it is of vital importance to investigate its fault diagnosis task. In this paper, a novel feature selection strategy is proposed to improve the multiclass support vector data description (SVDD) algorithm for planetary gearbox fault diagnosis. First, a novel feature selection method based on the cosine similarity measure in kernel space of Gaussian radial basis function (GRBF) is presented, so as to determine features that are sensitive to faults. Then, based on the selected features, an improved multiclass SVDD algorithm is developed to classify multiple classes of planetary gear faults, thus completing the fault diagnosis task. Finally, the effectiveness and advantage of the proposed method are demonstrated via experiments using wind turbine drivetrain diagnostics simulator (WTDDS), with comparison to several traditional methods.

This paper presents a fault diagnosis method called dominant trend based logistic regression (DTLR) for monitoring nonstationary processes. Different from conventional sample-wise diagnosis approaches, it uses sliding windows to collect process data and extract dominant trend features. After data preprocessing via robust sparse representation, the feature vector reflecting variation trend is obtained by solving a convex optimization problem, i.e., dominant trend extraction (DTE). Then the l 2 -norm of the dominant trend vector is used as a detection index to quantify the dissimilarity between normal and abnormal conditions. Once it exceeds the control limit, the feature vector is used to train the weight vector of logistic regression. The fault type can be determined as the class with the maximum conditional probability. With trend information, DTLR can effectively detect and isolate faults in nonstationary processes. Simulations on a synthetic nonstationary dynamic process, a nonstationary continuous stirred tank reactor (CSTR), and the real data of a blast furnace iron-making process illustrate superior monitoring and isolation performance of DTLR, compared with conventional methods.

This paper presents a new data-driven process monitoring method called recursive transformed component statistical analysis (RTCSA) for the purpose of incipient fault detection. Without space partition, RTCSA processes data in sliding windows to obtain orthogonal transformed components (TCs) recursively using rank-one modification. The statistical information of TCs can reveal some important process features, implying that faults can be detected by monitoring the statistics of TCs. With second-order statistics, the detection index reduces to relative changes of ordered eigenvalues of the sample covariance matrix. Fault detectability is analyzed in a statistical sense, leading to the analysis of the eigenvalues of stochastic matrices, including the closed-form expressions for the probability distribution function of the arbitrary l th largest eigenvalue of a class of real uncorrelated Wishart matrices. It indicates that a scaled ordered eigenvalue is sensitive to small changes. The structure of the detection index ensures that RTCSA is sensitive to incipient faults. Compared with existing multivariate statistical process monitoring approaches such as principal component analysis (PCA) and its variants, the superior detectability of RTCSA is illustrated by a numerical example and the Tennessee Eastman process.

In modern industry, detecting incipient faults timely is of vital importance to prevent serious system performance deterioration and ensure optimal process operation. Recently, multivariate statistical process monitoring (MSPM) techniques have been extensively studied and widely applied to modern industrial systems. However, conventional fault detection indices utilized in statistical process monitoring are not sensitive to incipient faults with small magnitude. In this paper, by introducing two representative smoothing techniques, novel incipient fault detection strategies based on a generic fault detection index in MSPM are proposed. Fault detectability for each proposed strategy is analyzed. In addition, the effects of the smoothing parameters on fault detection, including advantages and disadvantages, are also investigated. Finally, case studies on a numerical example and two practical industrial processes are carried out to demonstrate the effectiveness of the proposed incipient fault detection strategies.

For modern industrial processes, many sensors equipped operate in harsh environments and the large number of sensors increases the probability of sensor malfunction. In order to guarantee an optimal and efficient operating condition, incipient sensor fault detection and diagnosis become necessary and important. In the present work, a new data-driven process monitoring method called statistics Mahalanobis distance (SMD) is proposed for incipient fault detection of three common sensor fault types. Detectability analysis of SMD is provided and compared theoretically with the conventional approach. Besides, the effects of parameter selection in SMD on its fault detectability is briefly discussed. Then, a hierarchical strategy is proposed for subsequent fault diagnosis, including the fault isolation and fault classification aspects. Simulation studies on a numerical example and a benchmark process are carried out, which demonstrate the effectiveness and merits of the SMD based fault detection and diagnosis methods, in comparison with conventional approaches.

The moving average (MA)-type scheme, also known as the smoothing method, has been well established within the multivariate statistical process monitoring (MSPM) framework since the 1990s. However, its theoretical basis is still limited to smoothing independent data, and the optimality of its equally or exponentially weighted scheme remains unproven. This paper aims to weaken the independence assumption in the existing MA method, and then extend it to a broader area of dealing with autocorrelated weakly stationary processes. With the discovery of the non-optimality of the equally and exponentially weighted schemes used for fault detection when data have autocorrelation, the essence that they do not effectively utilize the correlation information of samples is revealed, giving birth to an optimally weighted moving average (OWMA) theory. The OWMA method is combined with the Hotelling’s T 2 statistic to form an OWMA T 2 control chart (OWMA-TCC), in order to detect a more challenging type of fault, i.e., intermittent fault (IF). Different from the MA scheme that puts an equal weight on samples within a time window, OWMA-TCC uses correlation (autocorrelation and cross-correlation) information to find an optimal weight vector (OWV) for the purpose of IF detection (IFD). In order to achieve a best IFD performance, the concept of IF detectability is defined and corresponding detectability conditions are provided, which further serve as selection criteria of the OWV. Then, the OWV is given in the form of a solution to nonlinear equations, whose existence is proven with the aid of the Brouwer fixed-point theory. Moreover, symmetrical structure of the OWV is revealed, and the optimality of the MA scheme for any IF directions when data exhibit no autocorrelation is proven. Finally, simulations on a numerical example and a continuous stirred tank reactor process are carried out to give a comprehensive comparison among OWMA-TCC and several existing static and dynamic MSPM methods. The results show a superior IFD performance of the developed methods.

Reliability of the high-speed train air brake system is very critical to ensure a safe and comfortable operation environment for passengers. In our recently published work, a new monitoring strategy based on the concept of inter-variable variance (IVV) was proposed. It was illustrated by theoretical analysis and extensive experiments that this strategy is effective for several kinds of faults, and superior to the KNORR logic which is adopted by most high-speed trains in practice. However, the aforementioned strategy based on conventional IVV still suffers from two drawbacks. More specifically, due to the specific property of IVV, this strategy is unable to detect certain faults even if the fault is very serious; in addition, the detectability of conventional IVV for incipient faults with relatively small magnitudes still needs to improve. In this paper, a fault detection method incorporating the idea of four stages partition and involving a new combined statistic is presented to perform fault detection for high-speed train air brake systems. Through theoretical reasoning, it is pointed out that the proposed method can overcome drawbacks of the conventional IVV method to a large extent. To demonstrate the effectiveness and merits of the proposed fault detection method, in comparison with existing methods, experimental studies are carried out on a practical high-speed train air brake test bench.

In process industries, it is necessary to conduct fault diagnosis after abnormality is found, with the aim to identify root cause variables and further provide instructive information for maintenance. Contribution plots along with multivariate statistical process monitoring are standard tools towards this goal, which, however, suffer from the smearing effect and high diagnostic complexity on large-scale processes. In fact, process variables tend to be naturally grouped, and in this work, a novel fault identification strategy based on group Lasso penalty along with a hierarchical fault diagnosis scheme is proposed by leveraging group information among variables. By introducing the group Lasso as a regularization approach, groups of irrelevant variables tend to yield exactly zero contributions collectively, which help find the exact root cause, alleviate the smearing effect, and furnish clear diagnostic information for process practitioners. For online computational convenience, an efficient numerical solution strategy is also presented. Besides, it turns out that the proposed approach also applies to dynamic monitoring models with lagged measurements augmented, thereby enjoying widespread generality. Its effectiveness is evaluated on both the Tennessee Eastman benchmark process and a pilot-scale experiment apparatus.

The need exists for perioperative blood management measures aimed at improving patient outcomes and reducing the risks of allogeneic blood transfusion (ABT). Our study aim is to discuss an algorithm to predict the need for perioperative blood transfusion in old patients with pertrochanteric femoral fractures.We retrospectively analysed the data from 220 elderly patients with pertrochanteric femoral fractures with regard to the probability of receiving an ABT within 72h after surgery. The patients were divided into ABT and non-ABT groups. A univariate analysis was used to compare between-group differences with regard to 13 variables. A logistic regression analysis and a probability algorithm to predict the need for an ABT based on independent predictors were used.The non-ABT group included 131 patients (55 males and 76 females), with an average age of 77.2±6.8 years; the ABT group included 89 patients (29 males and 60 females), with an average age of 79.7±6.6 years. The total volume of transfused blood was 276 Units; the actual average blood transfusion was 3.1±1.47 Units. Significant between-group differences (P0.05) were observed with regard to age, duration of operation, haemoglobin (Hb) at admission, intra-operative blood loss, type of fracture and type of anaesthesia. The mean volume of transfused blood in the proximal femoral nail anti-rotation (PFNA) and Gamma3 group was larger than that of the dynamic hip screw (DHS) group (P0.05). A logistic regression analysis revealed that patients with pertrochanteric femoral fractures who were elderly (81 years), had lower Hb levels at admission (≤124g/L), longer duration of operations (t85min), underwent intramedullary fixation (Gamma3 and PFNA) and had more intra-operative blood loss were more likely to need an ABT. This regression model predicted 74.1% of the transfused cases.An algorithm was devised to predict and manage the need for an ABT within 72h after surgery in patients with pertrochanteric femoral fractures. A reasonable transfusion program might reduce the complications caused by anaemia and effectively avoid the risks associated with ABTs.