动态MPCA在发酵过程监测与故障诊断中的应用

针对发酵过程非线性和时变特点,提出了一种具有实时性的动态MPCA方法,采用多模型非线性结构代替传统MPCA单模型线性化结构,克服了后者不能处理非线性过程和实时性的问题,并避免了MPCA在线应用时预报未来测量值带来的误差,提高了发酵过程性能监测和故障诊断的准确性。对头孢菌素C发酵过程的拟在线仿真研究,验证了基于动态MPCA的统计过程监测的有效性。     

A fault detection service for wide area distributed computations

The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to trade off timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver. This revised version was published online in July 2006 with corrections to the Cover Date.     

Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence

Process monitoring and profile analysis are crucial in detecting various abnormal events in semiconductor manufacturing, which consists of highly complex, interrelated, and lengthy wafer fabrication processes for yield enhancement and quality control. To address real requirements, this study aims to develop a framework for semiconductor fault detection and classification (FDC) to monitor and analyze wafer fabrication profile data from a large number of correlated process variables to eliminate the cause of the faults and thus reduce abnormal yield loss. Multi-way principal component analysis and data mining are used to construct the model to detect faults and to derive the rules for fault classification. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the model. Use of the proposed framework can effectively detect abnormal wafers based on a controlled limit and the derived simple rules. The extracted information can be used to aid fault diagnosis and process recovery. The proposed solution has been implemented in the semiconductor company. This has simplified the monitoring process in the FDC system through the fewer key variables. The results demonstrate the practical viability of the proposed approach.     

Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae

This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L^?1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T². The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T² control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

A parallel algorithm for robust fault detection in semiconductor manufacturing processes

The semiconductor manufacturing consists of a number of processes, and even a small fault occurring at any point can damage the product quality. The fast and accurate detection of such faults is essential to maintain high manufacturing yields. In this paper, we propose a parallel algorithm for fault detection in semiconductor manufacturing processes. The algorithm is a modification of the discord detection algorithm called HOT SAX, which adopted the SAX representation of time-series for efficient storage and computation. We first propose a sequential algorithm and then extend it to a parallel version. We evaluate our algorithm through experiments using the data obtained from a real-world semiconductor plasma etching process. As a result, our fault detection algorithm achieved 100 % accuracy without any false positive or false negative.     

Computational Effective Fault Detection by Means of Signature Functions

The paper presents a computationally effective method for fault detection. A system’s responses are measured under healthy and ill conditions. These signals are used to calculate so-called signature functions that create a signal space. The current system’s response is projected into this space. The signal location in this space easily allows to determine the fault. No classifier such as a neural network, hidden Markov models, etc. is required. The advantage of this proposed method is its efficiency, as computing projections amount to calculating dot products. Therefore, this method is suitable for real-time embedded systems due to its simplicity and undemanding processing capabilities which permit the use of low-cost hardware and allow rapid implementation. The approach performs well for systems that can be considered linear and stationary. The communication presents an application, whereby an industrial process of moulding is supervised. The machine is composed of forms (dies) whose alignment must be precisely set and maintained during the work. Typically, the process is stopped periodically to manually control the alignment. The applied algorithm allows on-line monitoring of the device by analysing the acceleration signal from a sensor mounted on a die. This enables to detect failures at an early stage thus prolonging the machine’s life.     

Fault detection and diagnosis in an industrial fed-batch cell culture process

A flexible process monitoring method was applied to industrial pilot plant cell culture data for the purpose of fault detection and diagnosis. Data from 23 batches, 20 normal operating conditions (NOC) and three abnormal, were available. A principal component analysis (PCA) model was constructed from 19 NOC batches, and the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) abnormal process conditions and to diagnose the root causes. This research demonstrates that data from a relatively small number of batches (approximately 20) can still be used to monitor for a wide range of process faults.     

Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data

DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies.     

Similarity Ratio Analysis for Early Stage Fault Detection with Optical Emission Spectrometer in Plasma Etching Process

A Similarity Ratio Analysis (SRA) method is proposed for early-stage Fault Detection (FD) in plasma etching processes using real-time Optical Emission Spectrometer (OES) data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR) statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A), takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line monitoring of Chinese hamster ovary cell cultivations – Part I

A new two‐dimensional fluorescence sensor system was developed for in‐line monitoring of mammalian cell cultures. Fluorescence spectroscopy allows for the detection and quantification of naturally occurring intra‐ and extracellular fluorophores in the cell broth. The fluorescence signals correlate to the cells’ current redox state and other relevant process parameters. Cell culture pretests with twelve different excitation wavelengths showed that only three wavelengths account for a vast majority of spectral variation. Accordingly, the newly developed device utilizes three high‐power LEDs as excitation sources in combination with a back‐thinned CCD‐spectrometer for fluorescence detection. This setup was first tested in a lab design of experiments study with process relevant fluorophores proving its suitability for cell culture monitoring with LOD in the μg/L range. The sensor was then integrated into a CHO‐K1 cell culture process. The acquired fluorescence spectra of several batches were evaluated using multivariate methods. The resulting batch evolution models were challenged in deviating and “golden batch” validation runs. These first tests showed that the new sensor can trace the cells’ metabolic state in a fast and reliable manner. Cellular distress is quickly detected as a deviation from the “golden batch”.     

Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts

Background

Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals.

Methods

This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed.

Results

The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described.

Conclusion

The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.     

Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis

On-line monitoring of penicillin cultivation processes is crucial to the safe production of high-quality products. In the past, multiway principal component analysis (MPCA), a multivariate projection method, has been widely used to monitor batch and fed-batch processes. However, when MPCA is used for on-line batch monitoring, the future behavior of each new batch must be inferred up to the end of the batch operation at each time and the batch lengths must be equalized. This represents a major shortcoming because predicting the future observations without considering the dynamic relationships may distort the data information, leading to false alarms. In this paper, a new statistical batch monitoring approach based on variable-wise unfolding and time-varying score covariance structures is proposed in order to overcome the drawbacks of conventional MPCA and obtain better monitoring performance. The proposed method does not require prediction of the future values while the dynamic relations of data are preserved by using time-varying score covariance structures, and can be used to monitor batch processes in which the batch length varies. The proposed method was used to detect and identify faults in the fed-batch penicillin cultivation process, for four different fault scenarios. The simulation results clearly demonstrate the power and advantages of the proposed method in comparison to MPCA.     

Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor

In recent years, multiscale monitoring approaches, which combine principal component analysis (PCA) and multi-resolution analysis (MRA), have received considerable attention. These approaches are potentially very efficient for detecting and analyzing diverse ranges of faults and disturbances in chemical and biochemical processes. In this work, multiscale PCA is proposed for fault detection and diagnosis of batch processes. Using MRA, measurement data are decomposed into approximation and details at different scales. Adaptive multiway PCA (MPCA) models are developed to update the covariance structure at each scale to deal with changing process conditions. Process monitoring by a unifying adaptive multiscale MPCA involves combining only those scales where significant disturbances are detected. This multiscale approach facilitates diagnosis of the detected fault as it hints to the time-scale under which the fault affects the process. The proposed adaptive multiscale method is successfully applied to a pilot-scale sequencing batch reactor for biological wastewater treatment.     

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcrafi Unmanned Aerial Vehicle （RUAV） sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.     

Dimensionally stable sensors for a continuous monitoring program to detect subterranean termite (Isoptera: Rhinotermitidae) activity

A dimensionally stable sensor composed of a closed-cell polyethylene sheet on which a silver particle circuit was painted and sandwiched between two spruce stakes was tested for use in a monitoring program to detect subterranean termites. Sensors were connected to a datalogger for continuous monitoring of sensor circuit breakages over 12 mo, and were manually inspected monthly to assess sensor performance. The mean monthly sensor accuracy for three field test sites was 98.7%, with most false responses caused by early timing of the monthly inspection when termites entered the station before damaging the sensor circuits. Mean sensor longevity (the time for a sensor circuit to break in the absence of termites) of the dimensionally stable sensors was 11.7 mo; a substantial improvement over the 4.4-mo longevity recorded previously for wooden sensors.     

Enumeration of Vibrio cholerae O1 in Bangladesh waters by fluorescent-antibody direct viable count.

A field trial to enumerate Vibrio cholerae O1 in aquatic environments in Bangladesh was conducted, comparing fluorescent-antibody direct viable count with culture detection by the most-probable-number index. Specificity of a monoclonal antibody prepared against the O1 antigen was assessed and incorporated into the fluorescence staining method. All pond and water samples yielded higher counts of viable V. cholerae O1 by fluorescent-antibody direct viable count than by the most-probable-number index. Fluorescence microscopy is a more sensitive detection system than culture methods because it allows the enumeration of both culturable and nonculturable cells and therefore provides more precise monitoring of microbiological water quality.     

Enumeration of Vibrio cholerae O1 in Bangladesh waters by fluorescent-antibody direct viable count

A field trial to enumerate Vibrio cholerae O1 in aquatic environments in Bangladesh was conducted, comparing fluorescent-antibody direct viable count with culture detection by the most-probable-number index. Specificity of a monoclonal antibody prepared against the O1 antigen was assessed and incorporated into the fluorescence staining method. All pond and water samples yielded higher counts of viable V. cholerae O1 by fluorescent-antibody direct viable count than by the most-probable-number index. Fluorescence microscopy is a more sensitive detection system than culture methods because it allows the enumeration of both culturable and nonculturable cells and therefore provides more precise monitoring of microbiological water quality.     

ADD CONTROL: advanced control solutions for waste water treatment

Instrumentation, control, and automation (ICA) in wastewater treatment enables the improvement of treatment plant performance without structural modifications of the plant. Even for wastewater treatment plants (WWTPs) meeting all criteria with respect to effluent concentrations and sludge disposal, ICA can be of interest as it can help to reduce energy consumption and operating costs of the plant. Simulations are a useful and cost-effective tool for designing and evaluating different control strategies. Simulation strategies developed with existing WWTP-specific simulation packages are based on ideal sensor and actuator behavior because signal noise and potential sensor and actuator failures are not considered. Real sensor and actuator behavior including failures, however, needs to be accounted for to ensure robust controller performance despite disturbances in sensor and actuator behavior. The ADD CONTROL project aims to design, implement, and validate a new simulation tool that allows for designing and testing “practical” control solutions. A multi-layer modeling architecture is proposed for the simulation tool to represent the hierarchical architecture for automation and control in full-scale WWTPs, and to separate mathematical modeling of components related to the treatment process from components describing instrumentation and actuation devices, and components related to automation and control. The developed simulation tool is implemented based on the TORNADO framework for modeling and virtual experimentation and the WEST^? product suite.     

Rapid and quantitative detection of homologous and non-homologous recombination events using three oligonucleotide MLPA

Embryonic stem (ES) cell technology allows modification of the mouse germline from large deletions and insertions to single nucleotide substitutions by homologous recombination. Identification of these rare events demands an accurate and fast detection method. Current methods for detection rely on Southern blotting and/or conventional PCR. Both the techniques have major drawbacks, Southern blotting is time-consuming and PCR can generate false positives. As an alternative, we here demonstrate a novel approach of Multiplex Ligation-dependent Probe Amplification (MLPA) as a quick, quantitative and reliable method for the detection of homologous, non-homologous and incomplete recombination events in ES cell clones. We have adapted MLPA to detect homologous recombinants in ES cell clones targeted with two different constructs: one introduces a single nucleotide change in the PCNA gene and the other allows for a conditional inactivation of the wild-type PCNA allele. By using MLPA probes consisting of three oligonucleotides we were able to simultaneously detect and quantify both wild-type and mutant alleles.     

Rapid biosensor for detection of antibiotic-selective growth of Escherichia coli

A rapid biosensor for the detection of bacterial growth was developed using micromechanical oscillators coated in common nutritive layers. The change in resonance frequency as a function of the increasing mass on a cantilever array forms the basis of the detection scheme. The calculated mass sensitivity according to the mechanical properties of the cantilever sensor is approximately 50 pg/Hz; this mass corresponds to an approximate sensitivity of approximately 100 Escherichia coli cells. The sensor is able to detect active growth of E. coli cells within 1 h. The starting number of E. coli cells initially attached to the sensor cantilever was, on average, approximately 1,000 cells. Furthermore, this method allows the detection of selective growth of E. coli within only 2 h by adding antibiotics to the nutritive layers. The growth of E. coli was confirmed by scanning electron microscopy. This new sensing method for the detection of selective bacterial growth allows future applications in, e.g., rapid antibiotic susceptibility testing.