In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance... [more]

Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of o... [more]

When a multi-agent system is subjected to faults, it is necessary to detect and classify the faults in time. This paper is motivated to propose a data-driven state prediction and sensor fault classification technique. Firstly, neural network-based state prediction model is trained through historical input and output data of the system. Then, the trained model is implemented to the real-time system to predict the system state and output in absence of fault. By comparing the predicted healthy output and the measured output, which can be abnormal in case of sensor faults, a residual signal can be generated. When a sensor fault occurs, the residual signal exceeds the threshold, a fault classification technique is triggered to distinguish fault types. Finally, the designed data-driven state prediction and fault classification algorithms are verified through a twin rotational inverted pendulum system with leader-follower mechanism.

The complex ventilation system development process is associated with the stages of modelling, design, execution, and testing. Each of these steps requires the use of measuring equipment that is capable of determining the basic parameters of the flow. In the process of calibrating instruments for measuring flow velocity, one of the limitations is the size of the calibrated devices positioned in the test section of the wind tunnel. This is related to the change in the flow condition within the vicinity of the calibrated anemometers, which is caused by the blockage effect. Instruments with significant dimensions in relation to the cross-sectional area of the wind tunnel test section may have an impact on the reference velocity as indicated by the standard. In such cases, the calibration results may be affected by additional systematic error. This article presents a study of this effect using a real case of a calibration laboratory and commonly used sensors. The influence of different typ... [more]

At present, most sensor calibration methods are off-line calibration, which not only makes them time-consuming and laborious, but also causes considerable economic losses. Therefore, in this study, an online calibration method of current sensors is proposed to address the abovementioned issues. The principle and framework of online calibration are introduced. One of the calibration indexes is angular difference. In order to accurately verify it, data acquisition must be precisely synchronized. Therefore, a precise synchronous acquisition system based on GPS timing is proposed. The influence of ionosphere on the accuracy of GPS signal is analyzed and a new method for measuring the inherent delay of GPS receiver is proposed. The synchronous acquisition performance of the system is verified by inter-channel synchronization experiment, and the results show that the synchronization of the system is accurate. Lastly, we apply our online calibration method to the current sensor; the experimen... [more]

As a new type of vision sensor, the dynamic and active-pixel vision sensor (DAVIS) outputs image intensity and asynchronous event streams in the same pixel array. We present a novel visual odometry algorithm based on the DAVIS in this paper. The Harris detector and the Canny detector are utilized to extract an initialized tracking template from the image sequence. The spatio-temporal window is selected by determining the life cycle of the asynchronous event streams. The alignment on timestamps is achieved by tracking the motion relationship between the template and events within the window. A contrast maximization algorithm is adopted for the estimation of the optical flow. The IMU data are used to calibrate the position of the templates during the update process that is exploited to estimate camera trajectories via the ICP algorithm. In the end, the proposed visual odometry algorithm is evaluated in several public object tracking scenarios and compared with several other algorithms. T... [more]

Traditional multivariate statistical methods, which are often used to monitor stationary processes, are not applicable to nonstationary processes. Cointegration analysis (CA) is considered an effective method to deal with nonstationary variables. If there is a cointegration relationship among the nonstationary series in the system, it indicates that a stable long-term dynamic equilibrium relationship exists among these variables. However, due to the complexity of modern industrial processes, there are nonlinear relations between variables, which are not considered by the traditional linear cointegration theory. Alternating conditional expectation (ACE) can perform nonlinear transformation on these variables to maximize the linear correlation of the transformed variables. It will be helpful to deal with the nonlinear relations by modeling with transformed variables. In this work, a new monitoring strategy based on ACE and CA is proposed. The data are first transformed by an ACE algorith... [more]

Under the condition of variable rotating speed, it is difficult to extract the degradation characteristics of the axial piston pump, which also reduces the accuracy of degradation recognition. To address these problems, this paper proposes a degradation state recognition method for axial piston pumps by combining spline-kernelled chirplet transform (SCT), adaptive chirp mode pursuit (ACMP), and extreme gradient boosting (XGBoost). Firstly, SCT and ACMP are proposed to deal with the vibration signal instability and high noise of the axial piston pump under variable rotating speed. The instantaneous frequency (IF) of the axial piston pump can be extracted effectively by obtaining the accurate time-frequency distribution of signal components. Then, stable angular domain vibration signals are obtained by re-sampling, and multi-dimensional degradation characteristics are extracted from the angular domain and order spectrum. Finally, XGBoost is used to classify the selected characteristics t... [more]

Biomanufacturers are being incited by regulatory agencies to transition from a quality by testing framework, where they extensively test their product after their production, to more of a quality by design or even quality by control framework. This requires powerful analytical tools and sensors enabling measurements of key process variables and/or product quality attributes during production, preferably in an online manner. As such, the demand for monitoring technologies is rapidly growing. In this context, we believe surface plasmon resonance (SPR)-based biosensors can play a role in enabling the development of improved bioprocess monitoring and control strategies. The SPR technique has been profusely used to probe the binding behavior of a solution species with a sensor surface-immobilized partner in an investigative context, but its ability to detect binding in real-time and without a label has been exploited for monitoring purposes and is promising for the near future. In this revi... [more]

Cosmetics analysis represents a rapidly expanding field of analytical chemistry as new cosmetic formulations are increasingly in demand on the market and the ingredients required for their production are constantly evolving. Each country applies strict legislation regarding substances in the final product that must be prohibited or regulated. To verify the compliance of cosmetics with current regulations, official analytical methods are available to reveal and quantitatively determine the analytes of interest. However, since ingredients, and the lists of regulated/prohibited substances, rapidly change, dedicated analytical methods must be developed ad hoc to fulfill the new requirements. Research focuses on finding innovative techniques that allow a rapid, inexpensive, and sensitive detection of the target analytes in cosmetics. Among the different methods proposed, immunological techniques are gaining interest, as they make it possible to carry out low-cost analyses on raw materials a... [more]

Modern societies need a constant and stable electrical supply. After relying primarily on formal mathematical modeling from operations research, control theory, and numerical analysis, power systems analysis has changed its attention toward AI prediction/forecasting tools. AI techniques have helped fix power system issues in generation, transmission, distribution, scheduling and forecasting, etc. These strategies may assist today’s large power systems which have added more interconnections to meet growing load demands. They make it simple for them to do difficult duties. Identification of problems and problem management have always necessitated the use of labor. These operations are made more sophisticated and data-intensive due to the variety and growth of the networks involved. In light of all of this, the automation of network administration is absolutely necessary. AI has the potential to improve the problem-solving and deductive reasoning approaches used in fault management. This... [more]

In this paper, a two-dimensional, two-layer quality regression model is established to monitor multi-phase, multi-mode batch processes. Firstly, aiming at the multi-phase problem and the multi-mode problem simultaneously, the relations among modes and phases are captured through the analysis between process variables and quality variables by establishing a two-dimensional, two-layer regression partial least squares (PLS) model. The two-dimensional regression traces the intra-batch and inter-batch characteristics, while the two-layer structure establishes the relationship between the target process and historical modes and phases. Consequently, online monitoring is carried out for multi-phase, multi-mode batch processes based on quality prediction. In addition, the online quality prediction and monitoring results based on the proposed method and those based on the traditional phase mean PLS method are compared to prove the effectiveness of the proposed method.

In order to eliminate driving dangers caused by tire surface bubbles, the detection method of bubble defects on tire surfaces based on line lasers and machine vision is studied. Since it is difficult to recognize tire surfaces directly through images, line laser scanning is used to obtain tire images. The filtering method and morphology method are combined to preprocess these images. The gray centroid method is adopted to extract the center of the laser stripe, and then the algorithm to determine the positions of bubble defects on tire surfaces is proposed. According to the geometric characteristics of tire bubbles, the coordinates of starting points, ending points, and rough positions of vertices are determined. Then, the ordinates of the laser center with sub-pixel accuracy near bubble vertices are discretely magnified. The mask made of Gaussian function is convoluted with the magnified region, and the maximum value is obtained. Furthermore, the position of bubble vertices can be acc... [more]

This paper focuses on the difficulties that appear when the number of fault samples collected by a permanent magnet synchronous motor is too low and seriously unbalanced compared with the normal data. In order to effectively extract the fault characteristics of the motor and provide the basis for the subsequent fault mechanism and diagnosis method research, a permanent magnet synchronous motor fault feature extraction method based on variational auto-encoder (VAE) and improved generative adversarial network (GAN) is proposed in this paper. The VAE is used to extract fault features, combined with the GAN to extended data samples, and the two-dimensional features are extracted by means of mean and variance for visual analysis to measure the classification effect of the model on the features. Experimental results show that the method has good classification and generation capabilities to effectively extract the fault features of the motor and its accuracy is as high as 98.26%.

Due to the complexity of materials and energy cycles, the distillation system has numerous working conditions difficult to troubleshoot in time. To address the problem, a novel DMA-SDG fault identification method that combines dynamic mechanism analysis based on process simulation and signed directed graph is proposed for the distillation process. Firstly, dynamic simulation is employed to build a mechanism model to provide the potential relationships between variables. Secondly, sensitivity analysis and dynamic mechanism analysis in process simulation are introduced to the SDG model to improve the completeness of this model based on expert knowledge. Finally, a quantitative analysis based on complex network theory is used to select the most important nodes in SDG model for identifying the severe malfunctions. The application of DMA-SDG method in a benzene-toluene-xylene (BTX) hydrogenation prefractionation system shows sound fault identification performance.

Rotary lime kilns are large-scale, energy-intensive unit operations that serve critical functions in a variety of industrial processes including cement production, pyrometallurgy, and kraft pulping. As massive expensive vessels that operate at high temperatures it is imperative from economic, environmental, and safety perspectives to optimize preventative maintenance and production efficiency. To achieve these objectives rotary kilns are increasingly outfitted with more sophisticated sensing technology that can provide additional operating insights. Although increasingly intricate data is collected from industrial operations the extent to which value is extracted from this data is often far from optimal. Our research aims to improve this situation by developing data analytics methods that leverage advanced industrial sensor data to address outstanding process faults. Specifically, this research investigates the use of infrared thermal cameras to detect and diagnose ring formation in r... [more]

In industrial process fault monitoring, it is very important to collect accurate data, but in the actual process, there are often various noises that are difficult to eliminate in the collected data due to sensor accuracy, measurement errors, or human factors. Existing statistical process monitoring methods often ignore the problem of data noise. To solve this problem, a sliding window wavelet denoising-global local preserving projections (SWWD-GLPP) process monitoring method is proposed. In the offline stage, the wavelet denoising method is used to denoise the offline data, and then, the GLPP method is used for offline modeling, and then, the control limit is obtained by the kernel density estimation method. In the online phase, the sliding window wavelet denoising method is used to denoise the online data in real time. Then, use the model of the GLPP method to find the statistics, compare them with the control limit, judge the fault situation, and finally, use the contribution graph... [more]

Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes, but ignore the deterioration of fault severity. This paper proposes a new two-stage hierarchical convolutional neural network for fault diagnosis of rotating machinery bearings. The failure mode and failure severity are modeled as a hierarchical structure. First, the original vibration signal is transformed into an energy spectrum matrix containing fault-related information through wavelet packet decomposition. Secondly, in the model training method, an adaptive learning rate dynamic adjustment strategy is further proposed, which adaptively extracts robust features from the spectrum matrix for fault mode and severity diagnosis. To verify the effectiveness of the method, the bearing fault data was collected using a rotating machine test bench. On this basis, the diagnostic accuracy, convergence performance and robustness of the model under different signal-to-noise ratios and variable load env... [more]

The article deals with the issues of improving the accuracy of measurements of the geometric parameters of objects by optoelectronic systems, based on a television multiscan. A mathematical model of a multiscan with scanistor activation is developed, expressions for its integral output current and video signal are obtained, and the mechanism of their formation is investigated. An expression for the video signal is obtained that reflects the dual nature of the discrete−continuous multiscan structure: the video signal can have a discrete (pulse) or analog (continuous) form, depending on the step voltage between the photodiode cells of the multiscan. A Vernier discrete−analog method for measuring the parameters of the light zone on a multiscan is proposed, in which in order to increase the accuracy of the measurements, the location of the video pulse is determined relative to the neighboring reference pulses of a rigid geometric raster due to the slope of the discrete structure of the mul... [more]

A quantitative analysis of vitamins A and E in commercial ointments containing 0.044% and 0.8% (w/w) of active pharmaceutical ingredients, respectively, was performed using partial least squares models based on FT Raman spectra. Separate calibration systems were prepared to determine the amount of vitamin A in a petrolatum base ointment and to quantify vitamins A and E in a eucerin base one. Compositions of the laboratory-prepared and commercial samples were controlled through a principal component analysis. Relative standard errors of prediction were calculated to compare the predictive ability of the obtained regression models. For vitamin A determination, these errors were found to be in the 3.8−5.0% and 5.7−5.9% ranges for the calibration and validation data sets, respectively. In the case of vitamin E modeling, these errors amounted to 3.7% and 4.4%. On the basis of elaborated models, vitamins A and E were successfully quantified in two commercial products with recoveries in the 9... [more]

The last 20-year announcement predicts a 3.5% increase in the number of yearly passengers which will result in the doubling of the number of passengers in air transport by 2037. Such anticipation indicates the need for efficient monitoring of airport infrastructure as the support of opportune and efficient maintenance works. The novelties of this article are a process model of maintenance and monitoring, suitable for smaller and less burdened airports, and the methodology of monitoring of runways by implementation of the geodetic and geomechanics falling weight deflectometer (FWD) method. In addition, the results confirm the assumption that a specific environment such as an airport allows for sufficiently reliable determination of deformation areas or areas of vertical deviations of runways in a relative short time period available for measurements by using geodetic methods only or by combining other methods; our research model includes the FWD method. With the research, we have also s... [more]

A prototype of a wireless vacuum microsensor combining the Pirani principle and surface acoustic waves (SAW) with extended range and sensitivity was designed, modelled, manufactured and characterised under different conditions. The main components of the prototype are a sensing SAW chip, a heating coil and an interrogation antenna. All the components were assembled on a 15 mm × 11 mm × 3 mm printed circuit board (PCB). The behaviour of the PCB was characterised under ambient conditions and in vacuum. The quality of the SAW interrogation signal, the frequency shift and the received current of the coil were measured for different configurations. Pressures between 0.9 and 100,000 Pa were detected with sensitivities between 2.8 GHz/Pa at 0.9 Pa and 1 Hz/Pa close to atmospheric pressure. This experiment allowed us to determine the optimal operating conditions of the sensor and the integration conditions inside a vacuum chamber in addition to obtaining a pressure-dependent signal.

Polymerization is a typical exothermic reaction in the fine chemical industry, which is easy to cause thermal runaway. In order to lower the thermal runaway risk of polymerization, a microreactor was adopted in this study to carry out styrene thermal polymerization. The hydrodynamic model and the fluid−solid coupling model of thermal polymerization of styrene were combined by using the computation fluid dynamics (CFD) method to build a three-dimensional steady-state model of the batch and the microreactor and compare. The results indicated that the maximum temperature of the polymerization in the microreactor was only 150.23 °C, while in the batch reactor, it was up to 371.1 °C. Therefore, the reaction temperature in the microreactor could be controlled more effectively compared with that in the batch reactor. During the reaction process, jacket cooling may fail, which would lead to an adiabatic situation. According to the divergence criterion (DIV), the thermal runaway of the polymeri... [more]

Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to evaluate the stability of the process and, after confirming that it is stable, to calibrate a monitoring procedure, i.e., estimate the reference model and set the control limits for the monitoring statistics. The basic assumption is that all relevant “common causes” of variation appear well represented in this reference dataset (using the terminology adopted by the founding father of process monitoring, Walter A. Shewhart). Otherwise, false alarms will inevitably occur during the implementation of the monitoring scheme. However, we argue and demonstrate in this article, that this assumption is often not met in modern industrial systems. Therefore, we introduce a new approach based on the r... [more]

The barely satisfactory monitoring situation of the hypertoxic fluorochemical engineering processes requires the application of advanced strategies. In order to deal with the non-linear mechanism of the processes and the highly complicated correlation among variables, a wavelet transform-assisted convolutional neural network (CNN) based multi-model dynamic monitoring method was proposed. A preliminary CNN model was first trained to detect faults and to diagnose part of them with minimum computational burden and time delay. Then, a wavelet assisted secondary CNN model was trained to diagnose the remaining faults with the highest possible accuracy. In this step, benefitting from the scale decomposition capabilities of the wavelet transform function, the inherent noise and redundant information could be filtered out and the useful signal was transformed into a higher compact space. In this space, a well-designed secondary CNN model was trained to further improve the fault diagnosis perfor... [more]