Various types of risks exist in a supply chain, and disruptions could lead to economic loss or even breakdown of a supply chain without an effective mitigation strategy. The ability to detect disruptions early can help improve the resilience of the supply chain. In this paper, the application of principal component analysis (PCA) and dynamic PCA (DPCA) in fault detection and diagnosis of a supply chain system is investigated. In order to monitor the supply chain, data such as inventory levels, market demands and amount of products in transit are collected. PCA and DPCA are used to model the normal operating conditions (NOC). Two monitoring statistics, the Hotelling's T-squared and the squared prediction error (SPE), are used to detect abnormal operation of the supply chain. The confidence limits of these two statistics are estimated from the training data based on the $\chi^2$- distributions. The contribution plots are used to identify the variables with abnormal behavior when at le... [more]

Graphene is a material gaining attention as a candidate for new application fields such as chemical sensing. In this review, we discuss recent advancements in the field of hydrogen gas sensors based on graphene. Accordingly, the main part of the paper focuses on hydrogen gas sensors and examines the influence of different manufacturing scenarios on the applicability of graphene and its derivatives as key components of sensing layers. An overview of pristine graphene customization methods is presented such as heteroatom doping, insertion of metal/metal oxide nanosized domains, as well as creation of graphene-polymer blends. Volumetric structuring of graphene sheets (single layered and stacked forms) is also considered as an important modifier of its effective use. Finally, a discussion of the possible advantages and weaknesses of graphene as sensing material for hydrogen detection is provided.

Degradation state recognition and failure prediction are the key steps of prognostic and health management (PHM), which directly affect the reliability of the equipment and the selection of preventive maintenance strategy. Given the problem that the distinction between feature vectors is not obvious and the accuracy of fault prediction is low, a method based on multi-class Gaussian process classification and Gaussian process regression (GPR) is studied by the vibration signal and flow signal in six degraded states of the axial piston pump. For degradation state recognition, the variational mode decomposition (VMD) was used to decompose the vibration signal, and obtaining intrinsic mode function (IMF) components with rich information. Subsequently, multi-scale permutation entropy (MPE) was employed to select feature vectors of IMF components in different states. In order to reduce feature dimensions and improve recognition performance, ReliefF was used to select feature vectors with hig... [more]

In conditional microbial screening, a limited number of candidate strains are tested at different conditions searching for the optimal operation strategy in production (e.g., temperature and pH shifts, media composition as well as feeding and induction strategies). To achieve this, cultivation volumes of >10 mL and advanced control schemes are required to allow appropriate sampling and analyses. Operations become even more complex when the analytical methods are integrated into the robot facility. Among other multivariate data analysis methods, principal component analysis (PCA) techniques have especially gained popularity in high throughput screening. However, an important issue specific to high throughput bioprocess development is the lack of so-called golden batches that could be used as a basis for multivariate analysis. In this study, we establish and present a program to monitor dynamic parallel cultivations in a high throughput facility. PCA was used for process monitoring and a... [more]

In mammalian cell culture, especially in pharmaceutical manufacturing, pH is a critical process parameter that has to be controlled as accurately as possible. Not only does pH directly affect cell culture performance, ensuring a comparable pH is also crucial for scaling and transfer of processes. A sample-based offline pH measurement is commonly used to ensure correct bioreactor pH probe signals after sterilization and as a detection measure for drifts of probe signals. However, the sample-based pH offline measurement does not necessarily deliver required accuracy. Offsets between bioreactor pH and sample pH heavily depend on equipment, local procedures and the offline measurement method that is used. This article adequately describes a novel, non-invasive method to determine pH and pCO2 in sterile bioreactors without the need to sample and measure offline. This method utilizes the chemical correlation between carbon dioxide in the gas phase, dissolved carbon dioxide, bicarbonate and d... [more]

State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightl... [more]

Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hard... [more]

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be... [more]

This article offers reasons to defend the use of generic behavior models as opposed to specific models in applications to determine component degradation. The term generic models refers to models based on operating data from various units, whereas specific models are calculated using operating data taken from a single unit. Moreover, generic models, used in combination with a status indicator, show excellent capacity for detecting anomalies in the equipment and for evaluating the effectiveness of the maintenance actions, resulting in lower development and maintenance costs for the operating firm. Artificial neural networks and moving means were used to calculate the degradation indicators, based on the remainders in the model. The models were developed from operating data from fourteen wind turbines monitored over several years, and applied to the detection of faults in the bearings on the non-drive end of the generator. The use of generic models may not be recommendable for detecting... [more]

The electrical system of a wind farm has a significant influence on the wind farm reliability and electrical energy yield. The disconnect switch installed in an electrical system cannot only improve the operating flexibility, but also enhance the reliability for a wind farm. Therefore, this paper develops a probabilistic transfer technique for integrating the electrical topology structure, the isolation operation of disconnect switch, and stochastic failure of electrical equipment into the reliability assessment of wind farm electrical system. Firstly, as the traditional two-state reliability model of electrical equipment cannot consider the isolation operation, so the paper develops a three-state reliability model to replace the two-state model for incorporating the isolation operation. In addition, a proportion apportion technique is presented to evaluate the state probability. Secondly, this paper develops a probabilistic transfer technique based on the thoughts that through transfe... [more]

Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25⁻35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power,... [more]

Induction motors consume a great portion of the generated electrical energy. Moreover, most of them work at underloaded conditions, so they have low efficiencies and waste a lot of energy. Because of this, the efficiency estimation of in-service induction motors is a matter of great importance. This efficiency estimation is usually performed through indirect methods, which do not require invasive measurements of torque or speed. One of these methods is the modified Air-Gap Torque (AGT) method, which only requires voltage and current data, the stator resistance value, and the mechanical losses. This paper approaches the computation of a modified stator resistance including the mechanical losses effect to be applied in the AGT method for torque and efficiency estimation of induction motors. Some improvements are proposed in the computation of this resistance by using a direct method, as well as the possibility to estimate this parameter directly from the nameplate data of the induction m... [more]

In this work, a systematic approach based on the geometric observer is proposed to design a model-based soft sensor, which allows the estimation of quality indexes in a bioreactor. The study is focused on the structure design problem where the set of innovated states has to be chosen. On the basis of robust exponential estimability arguments, it is found that it is possible to distinguish all the unmeasured states if temperature and dissolved oxygen concentration measurements are combined with substrate concentrations. The proposed estimator structure is then validated through numerical simulation considering two different measurement processor algorithms: the geometric observer and the extended Kalman filter.

Conventional leak detection techniques require improvements to detect small leakage (<10%) in gas mixture pipelines under transient conditions. The current study is aimed to detect leakage in gas mixture pipelines under pseudo-random boundary conditions with a zero percent false alarm rate (FAR). Pressure and mass flow rate signals at the pipeline inlet were used to estimate mass flow rate at the outlet under leak free conditions using Hammerstein model. These signals were further used to define adaptive thresholds to separate leakage from normal conditions. Unlike past studies, this work successfully detected leakage under transient conditions in an 80-km pipeline. The leakage detection performance of the proposed methodology was evaluated for several leak locations, varying leak sizes and, various signal to noise ratios (SNR). Leakage of 0.15 kg/s—3% of the nominal flow—was successfully detected under transient boundary conditions with a F-score of 99.7%. Hence, it can be conclude... [more]

Monitoring process variables in bioprocesses with complex expression systems, such as filamentous fungi, requires a vast number of offline methods or sophisticated inline sensors. In this respect, cell viability is a crucial process variable determining the overall process performance. Thus, fast and precise tools for identification of key process deviations or transitions are needed. However, such reliable monitoring tools are still scarce to date or require sophisticated equipment. In this study, we used the commonly available size exclusion chromatography (SEC) HPLC technique to capture impurity release information in Penicillium chrysogenum bioprocesses. We exploited the impurity release information contained in UV chromatograms as fingerprints for development of principal component analysis (PCA) models to descriptively analyze the process trends. Prediction models using well established approaches, such as partial least squares (PLS), orthogonal PLS (OPLS) and principal component... [more]

Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients o... [more]

Hydrocyclones are extensively known as important separation devices which are used in many industrial fields. However, the general method to estimate device performance is time-consuming and has a high cost. The aim of this paper was to investigate the blockage diagnosis for a lab-scale hydrocyclone using a vibration-based technique based on wavelet denoising and the discrete-time Fourier transform method. The results indicate that the farther away the installation location from feed inlet the more regular the frequency is, which reveals that the installation plane near to the spigot generated the regular frequency distribution. Furthermore, the acceleration amplitude under blockage degrees 0%, 50% and 100% fluctuates as a sine shape with increasing time, meanwhile the vibration frequency of the hydrocyclone rises with increasing throughput. Moreover, the distribution of four dimensional and five non-dimensional parameters for the time domain shows that the standard deviation, compared... [more]

Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter th... [more]

As one of the semiconductor back-end processes, die attach process is the process that attaches an individual non-defective die (or chip) produced from the semiconductor front-end production to the lead frame on a strip. With most other processes of semiconductor manufacturing, it is very important to improve productivity by lessening the occurrence of defective products generally represented as losses, and then find the fault causes which lower productivity of the die attach process. Thus, as the case study to analyze quantitatively the faults of the die attach process equipment, in this research, we developed analysis systems including statistical analysis functions to improve the productivity of die attach process. This research shows that the developed system can find the causes of equipment faults in die attach process equipment and help improve the productivity of the die attach process by controlling the critical parameters which cause unexpected equipment faults and losses.

It is of critical importance to keep a steady operation in the blast furnace to facilitate the production of high quality hot metal. In order to monitor the state of blast furnace, this article proposes a fault detection and identification method based on the multidimensional Gated Recurrent Unit (GRU) network, which is a kind of recurrent neural network and is highly effective in handling process dynamics. Comparing to conventional recurrent neural networks, GRU has a simpler structure and involves fewer parameters. In fault detection, a moving window approach is applied and a GRU model is constructed for each process variable to generate a series of residuals, which is further monitored using the support vector data description (SVDD) method. Once a fault is detected, fault identification is performed using the contribution analysis. Application to a real blast furnace fault shows that the proposed method is effective.

The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current mo... [more]

There is a profound need in bioprocess manufacturing for low-cost single-use sensors that allow timely monitoring of critical product and production attributes. One such opportunity is screen-printed enzyme-based electrochemical sensors, which have the potential to enable low-cost online and/or off-line monitoring of specific parameters in bioprocesses. In this study, such a single-use electrochemical biosensor for lactate monitoring is designed and evaluated. Several aspects of its fabrication and use are addressed, including enzyme immobilization, stability, shelf-life and reproducibility. Applicability of the biosensor to off-line monitoring of bioprocesses was shown by testing in two common industrial bioprocesses in which lactate is a critical quality attribute (Corynebacterium fermentation and mammalian Chinese hamster ovary (CHO) cell cultivation). The specific response to lactate of the screen-printed biosensor was characterized by amperometric measurements. The usability of th... [more]

Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D−S evidence theory. Finally, the singularity index of the cross-correlation function and th... [more]

Ice cream viscosity is one of the properties that most changes during crystallization in scraped surface heat exchangers (SSHE), and its online measurement is not easy. Its estimation is necessary through variables that are easy to measure. The temperature and power of the stirring motor of the SSHE turn out to be this type of variable and are closely related to the viscosity. Therefore, a mathematical model based on these variables proved to be feasible. The development of this mathematical relationship involved the rheological study of the ice cream base, as well as the application of a method for its in situ melting in the rheometer as a function of the temperature, and the application of a mathematical model correlating the SSHE stirring power and the ice cream viscosity. The result was a coupled model based on both the temperature and stirring power of the SSHE, which allowed for online viscosity estimation with errors below 10% for crystallized systems with a 30% ice fraction at... [more]