Raceway for instrumentation lines

CATEGORY: INSTRUMENTATION 
Raceway for instrumentation lines

Type	Patent
Inventor	Richard C. Lacey Jr
Inventor	Patrick D'olive
URL	Free Full Text Source:  http://www.google.com/patents/US20150233497
Assignee	Techline International, Inc.
Patent Number	US20150233497 A1
Issue Date	Aug 20, 2015
Abstract	A kit for forming a raceway assembly including a plurality of members can include a plurality of portions including channel portions and coupling portions, each of the portions extending in an axial direction and having a plurality of walls defining a generally U-shaped cross-section. Each channel portion may have at least one opening in at least one wall, and each of the coupling portions has at least one hole in at least one wall. Each coupling portion may be adapted to receive one or more of the channel portions so that axial directions of the received channel portions are aligned with the axial direction of the coupling portion and so that at least one of the openings of each received channel portion is aligned with at least one of the holes. The coupling portions and channel portions may have inter-engageable features which preclude movement of the received channel portions relative to the coupling portion other than in the axial directions thereof. In addition, the kit may include one or more non-threaded pins configured to pass through the aligned holes and openings and lock in place.

Instruments for monitoring electrostatic phenomena in reactors (ExxonMobil)

CATEGORY: INSTRUMENTATION 
Instruments for monitoring electrostatic phenomena in reactors (ExxonMobil)

Type	Patent
Inventor	William Anthony Lamberti
Inventor	Joseph Andres Moebus
URL	Free Full Text Source:  http://www.google.com/patents/US20140193924
Assignee	Exxonmobil Research And Engineering Company
Patent Number	US20140193924 A1
Issue Date	Jul 10, 2014
Abstract	Probes for monitoring electrostatic phenomena in challenging environments, such as fluidized bed reactors. These probes include a coated or uncoated static probe for measuring electric field and or particle charge state, an oscillatory electric field probe for measuring electric field, a chopped electric field probe for measuring electric field, and a radio-frequency antenna probe for detecting electrostatic discharges. BACKGROUND Fouling in commercial fluidized bed reactors, including gas phase polymerization reactors, is a significant operational issue. Fouling negatively impact operational efficiency and ultimately requires time-consuming shutdown and maintenance. Understanding the causal factors of fouling within the reactor systems would be beneficial in reducing fouling. Fouling in fluidized bed reactors can be strongly affected by physical processes within the fluidized bed reactor, such as electrostatic charge and solids carryover within a recycle loop. Commercial probes, including those commonly referred to as static probes and acoustic probes, exist for measuring certain physical parameters within fluidized bed reactors, such as electrostatic charge and solids flow. In common practice, however, these probes do not reliably directly measure these phenomena, and are instead dominated by noise and/or artifacts in the signals. Thus, probe signals have proven to be of limited or no value in monitoring the operational status of or diagnosing inefficiencies in fluidized bed reactors. Electrostatic charge can affect commercial process units such as chemical reactors, granular particle handling equipment, transfer lines, holding tanks, and shipping containers, for example. The types of operations can include fluidized bed reactors for producing a variety of chemical products such as gas, liquid or solid products such as polyethylene. Cryogenic processes or handling equipment are another notable case where the dry environment can lend itself to electrostatic charge buildup in at least some portions of a process, especially if solids such as ice form due to the cryogenic conditions. The buildup of electrostatic charge on particles, and/or process components results in the formation of an electric field, which then exerts forces on particles or components within a given process or system. Additionally, in cases where the electrostatic charge is sufficient, electrostatic discharge events can occur, which by themselves can be deleterious to reliable or safe operations, or simply an indicator that electrostatic effects are present at a given moment. For example, commercial polyethylene (PE) reactors utilize a fluidized bed to suspend catalyst particles that grow into PE resin particles by converting ethylene gas into polyethylene resin. Collisions between catalyst particles, resin particles and also the reactor wall can result in the particles becoming charged. The wall can also become charged wherever it has an insulating coating or surface deposit or layer. If the net charge per volume (ρ) in a cylindrical reactor is uniform, the electric field is given by: E(r)=(ρr)/2∈, where r is the cylinder radius, E(r) is the electric field as a function of reactor vessel radius and ∈ is the relative permittivity of the volume. This electric field is greatest at the reactor wall, and creates a force (F) on the charged particles given by F(r)=qE(r), where q is the particle charge. Both F and E are still a function of radius as mentioned above. Particles with charge of the same sign as the bulk net charge density experience a force towards the wall. If this force is large enough, it can pin the charged catalyst and resin particles to the wall, and they tend to grow into PE sheets (sheeting) that eventually fall off and clog up the resin discharge system, forcing a shutdown of the reactor. In addition, if the electric field is larger than the Paschen breakdown strength of the gas in the reactor, electrical discharges, or sparks can occur through the gas. Any isolated conductors in the reactor can become charged by particle impact, and they can also spark to nearby metallic objects. In addition, the insulating coating on the reactor wall can charge to a level that supports propagating brush discharges across and through the wall surface. It is desirable to instrument the reactor with sensors that can indicate a highly charged condition, because that can eventually lead to sheeting and a forced reactor shutdown. Advanced knowledge of a sheeting condition allows operating parameters to be adjusted to eliminate the condition. A highly charged reactor condition can be accompanied by sparking inside the reactor, while lower levels of charge would not result in sparks. Therefore, sparking can be used as an indicator of a highly charged reactor, and indirectly, as a warning that the reactor is in a condition conducive to sheeting. It is well known that electrical sparks emit electromagnetic waves, typically in the radio frequency (RF) part of the electromagnetic spectrum between about 100 kHz and 10 GHz. Due to the challenging environments encountered in chemical process equipment, especially within a high temperature fluidized bed with reactive gas mixture, no prior art exists for detection of RF signals arising from electrical discharges. In simpler environments, such as assembly rooms for sensitive semiconductor components, some technology does exist. For these simpler environments, the current art includes technology such as the 3M company's EM Aware1, which contains radio frequency receivers with appropriate antennas used to detect sparks by receiving these radio waves. The amplitude, spectral distribution and radiation pattern of the emitted waves depends on the source of the spark. 13M™ EM Aware TNG ESD Event Monitor. Models 3M034-3-TNG, 3M034-030-TNG and 3M034-031-TNG As indicated in the EM Aware user guide published by the manufacturer, this technology is intended to be used only as follows. “Intended Use”—The 3M EM Aware TNG ESD Event Monitor monitors up to four key parameters that keep you aware of critical symptoms of ESD problems: 1) ESD events; 2) static voltages; 3) ionization balance; and 4) charge decay. The thresholds for these parameters are fully adjustable to suit your needs. The improved design features a metal case module with built-in LCD display, a control joystick, remote antenna, power supply and a data output. The monitor system must be installed as specified in this user's guide. It is intended for use in the following environmental conditions only: (1) indoor use; (2) altitudes up to 2,000 meters above sea level; (3) temperature range of 10° C. to 40° C.; (4) maximum relative humidity of 80% for temperatures up to 31° C., decreasing linearly to 50% relative humidity at 40° C.; and (5) pollution degree two (office, laboratory, test station). It would be desirable to have instruments which can reliably measure and monitor electrostatic phenomena within these systems. These instruments could also be combined with new methods for processing and interpreting probe signals in fluidized bed reactor systems. It would further be desirable to have new methods, which may rely upon the use of these instruments and processing methods to provide for more efficient system operation and reliability.

Method and System of Using Inferential Measurements for Abnormal Event Detection in Continuous Industrial Processes (Exxonmobil Research And Engineering Company)

CATEGORY: INSTRUMENTATION
PATENT
Method and System of Using Inferential Measurements for Abnormal Event Detection in Continuous Industrial Processes (Exxonmobil Research And Engineering Company)
Publication number
US20120330631 A1
Application number
13/525,878
Publication date
Dec 27, 2012
Inventors
Kenneth F. Emigholz
Original Assignee
Exxonmobil Research And Engineering Company
Abstract
The present invention is a method for developing a system for detecting an abnormal on-line analysis or laboratory measurement and for predicting an abnormal quality excursion due to an abnormal process condition.
FIELD OF THE INVENTION
This invention generally relates to the early detection of abnormal events in continuous industrial processes and more specifically relates to systems for detecting incorrect measurement values of key operating parameters and to predicting future abnormal excursions of key operating parameters, and the methods for developing such systems.
BACKGROUND OF THE INVENTION
When continuous industrial processes are operated near their economic optimum, they are operated at maximum or minimum limits of key operating parameters, such as the product quality specification. Consequently, knowing the current and expected future value of these parameters is very important to both the efficient operation of continuous industrial processes, such as refineries and chemical plants, as well as the prevention of abnormal events. For example, abnormal quality excursions can cause products to be outside their specification limits, cause the sudden malfunctioning of process equipment (such as pump cavitation due to vapor formation), and cause the degradation of process performance (such as loss of reaction from coke buildup on catalyst or loss of heat transfer from coke formation in furnace tubes).
The direct measurement of process stream quality and other key operating parameters can be both expensive and trouble prone. On-line analysis incurs both a high initial installation cost and a high maintenance cost. The on-line analysis often requires a dedicated process sampling system and an environmentally protected field shelter for the analysis equipment. Maintenance of this equipment can require specially trained personnel and high preventative maintenance effort; however it is often the case that maintenance is done only in response to a known problem with the on-line analyzers. Recent on-line analyzer systems incorporate standard samples for testing and calibration, and micro computers which run continual equipment diagnostics.
Often sites choose to make quality measurements using a laboratory analysis, either in conjunction with an on-line analysis or instead of an on-line analysis. Because of the extensive human involvement in taking field samples and then analyzing these samples, these lab analyses are usually infrequent (from daily to weekly), have significant normal variability, and have a high error rate.
To supplement the on-line analysis and laboratory analysis approaches, an inferential estimate of the quality parameter can be created from more readily available process measurements (primarily temperatures, pressures, and flows). The two traditional uses for inferential measurements are first to create a continuous estimate for the more slowly sampled analyzer value for use within closed loop process control applications, and second to validate analyzer and laboratory values. For these uses, by quickly updating the models with the actual on-line analyzer values or laboratory measurements, reasonably adequate performance can be achieved even with poor performing models, if the model has some power to estimate the next analyzer sample, it would behave no worse than using the last analyzer sample as an estimate for the next analyzer sample. However, except for ensuring new analyzer sample values are within minimum and maximum change limits, models that use rapid updating are inadequate for detecting abnormal analyzer sample values or for predicting abnormal quality excursions because of abnormal process events.
For these uses, there cannot be any issue distinguishing a real abnormal event from a defect in the model. This requires that only highest quality training data be used to build the model.
The majority of inferential measurements in the continuous process industries are developed by using process data driven methods such as neural nets, stepwise regression, partial least squares etc. where both the model structure and the model parameters are determined from operating data. Alternatively inferential measurements can be based on first principles engineering models where only the model parameters are determined from operating data. The quality of the models developed using these approaches is significantly affected by the quality of the data selected to build the model or to fit parameters in a first principles engineering model. The data selection, data analysis and data conditioning methods need to be tailored to the characteristics of data, rather than relying on generic approaches based on simple statistical assumptions. The failure to develop high quality inferential estimates in the continuous process industries can often be traced to ineffective data selection and data. conditioning methods that don't match the characteristics of process data.
SUMMARY OF THE INVENTION
The objective of an abnormal event detection method or system, AED, (see e.g. US 2006/0058898) is to prevent the escalation of process and equipment problems into serious incidents. It achieves this by first providing the process operator with an early warning of a developing process problem or equipment problem, before the alarm system is activated, and then by providing the operator with key information for localizing and diagnosing the root cause of the problem.
In this invention, abnormal event detection is used to describe a method and system for detecting abnormal values of key operating parameters, particularly from on-line analyzers or laboratory analyses and for predicting an abnormal excursion in key process parameters, such as product quality, caused by recent abnormal process conditions. In summary, this invention includes: an inferential model for the abnormal event detection of operating parameter measures which combines pretreatment of the inputs to account for time dynamics with a prior art algorithm, such as PLS, a method for building high quality inferential models of operating parameters to detect abnormal values and to predict abnormal excursions (FIG. 1) and a system for online implementation of the model (shown in FIG. 2).
The system for online implementation includes:
• ◦1. reprocessing/time synchronizing the real-time data
◦2. calculating estimates of the current quality value, the future predicted quality value, and the estimate of the quality for use in a process control application
◦3. on-line updating of the model based on the actual measurement
◦4. interpreting the model estimate results
◦5. combining and summarizing the normal/abnormal status of multiple operating parameter measurements
◦6. providing abnormal event diagnostic information to the console operator
◦7. providing model diagnostic information to the maintenance engineer
At the heart of this invention is an inferential model Which estimates the value of a key operating parameter that is measured by an on-line analyzer or by a laboratory analysis. This model uses readily available process measurements such as temperatures, pressures and flows. There are many prior art algorithms for calculating such an inferential measurement model, such as neural networks, NN, partial least squares, PLS. and linear regression. However, these methods have difficulty incorporating the time dynamics which are characteristic of continuous industrial processes.
For the inferential model, this invention combines a prior art method, in particularly PLS but not limited to it, with three different pre-treatments of the model input data to handle the time dynamics inherent in continuous industrial processes, a step known as time synchronization. Each form of time synchronization is structured to the particular use the model will be put to. This results in a two part model calculation, first calculating each time synchronized input and then combining these time synchronized inputs into an estimate of the output value. The different time synchronizations the inputs are done to estimate the current value of the analyzer/laboratory measurement, to predict the future value of the analyzer measurement, to be used as the input to a model based predictive control algorithm, or to be used as the input in standard control algorithm, such as a proportional integral derivative (PID) algorithm
To train the inferential model, this invention includes a model development approach which creates a model training dataset structured to the characteristics of the data generated by continuous industrial processes. This includes:
• ◦using normal operating data instead of designed experiment data
◦accounting for protracted steady state operations at a small number of operating points
◦accounting for process time dynamics
◦accounting for the cross correlation among model inputs
◦accounting for unmeasured effects on the analysis/operating parameter
The on-line system preprocesses the data and calculates the inferential model in a manner consistent with the off-line model development. Additionally, the on-line system includes a method for adapting the model in real time and methods for interpreting the inferential model calculation and the analyzer/lab measurement as to whether an abnormal event is present or not. Once the operator receives an indication of an abnormal event, the system allows the operator to drill down to the underlying abnormal indication and the supporting data. The operator has the responsibility to decide on the correct action based on his analysis of the abnormal indication and the supporting data.
A typical abnormal event detection application could have 10-20 key process parameters/qualities within the process scope of an individual console operator. Using fuzzy petri nets, these key process parameter models are either:
• ◦merged together to provide a single summary trend of the normal/abnormal status of all key process parameters or
◦merged with other models in the same process sub-area to provide summary trends of the normal/abnormal status of that process sub-area.
In this manner, the on-line system can provide the process operator with a summarized normal/abnormal status of the process condition.
This invention includes preferred methods for developing inferential models of operating parameters and using such inferential models to predict future abnormal excursions in continuous industrial processes.
In a preferred embodiment herein is a method for developing an interferential model of operating parameters in order to detect abnormal values and to predict future abnormal excursions in continuous industrial processes comprising:
a) identifying inferential measurement operating regions;
b) identifying surrogate online measurements to be used as a substitute for measurements from offline laboratory analyses;
c) collecting process input data for use in the model, including input data from process upsets and operating point changes;
d) identifying unusual process input data and discarding process input data values that do not represent the actual process being measured;
e) identifying periods of steady state operation in the process input data;
f) eliminating most periods of process input data that include steady state operation with an average rate of change (ROC) between minus and plus the ROC standard deviation;
g) identifying normal ranges for the process input data. and model output data and creating data filters to exclude the process input data related to unusual process operations;
h) identifying and eliminating the process input data with poor signal to noise ratios, and filtering the data with smoothing/spike fitters;
i) creating mathematical transforms of the process input data to improve the fit of the model;
j) building dynamic single-input, single-output (SISO) models between the process input data and its associated on-line analyzer value to handle process time dynamics;
k) removing slow trends and biases from the process input data by using a low pass filter;
l) creating a dataset from methods of steps a) to k); and
m) building the inferential model by using the dataset created in step l).
Free Full Text Source: http://www.google.com/patents/US20120330631?dq=inassignee:exxonmobil&hl=en&sa=X&ei=IdCTUaj5JpO30QGZ8oH4DA&ved=0CGYQ6AEwBzge

Towards predictable and reliable wireless communication in harsh environments

CATEGORY: INSTRUMENTATION
THESIS
Towards predictable and reliable wireless communication in harsh environments
Martin Ekstrom
Dissertation (2013)
Malardalen University
Abstract
Wireless communication in industrial, scientific and medical applications have several benefits.  The main benefits when using wireless technologies include ease-of-deployment, the simplicity to introduce new units into the network, and mobility. However, it also puts higher demands on the communications network, including reliability and predictability compared to wired communications. The reliability issues correlate to the radio communication and the possibility to ensure that the user data is received, and within the time fram of the system requirements.
This doctoral thesis presents and empirical measurement approach to investigate and model the behavior linked to reliability and predictability. The focus of the work presented is energy consumption, packet-error-rate and latency studies.  This is performed for various radio technologies and standards in harsh environments. The main contributions of this thesis are the measurements platforms and procedures that have been developed to meet the requirements to investigate modern radio technologies in terms of predictability and reliability.
This thesis shows that it is possible to predict wireless communication in radio harsh environments.  However, it is necessary to determine the characteristics of the environment to be able to choose a suitable radio technology.\ The measurement procedures presented in this thesis alongside the platform developed enable these types of investigation.  In this thesis a model of the energy consumption for a Bluetooth radio in low-duty-cycle applications with point-to-multipoint communication is presented. The measurements show that distance and transmission power will not affect energy consumption for a Bluetooth or ZigBee module.  However, the packet-error-rate and number of retransmissios will affect the overall energy consumption, and these parameters can be correlated to distance and environmental characteristics. This thesis also present two application-based solutions, a time synchronized ECG network with reliable data communication as well as a low-latency wireless I/O for a hydro plant.
Free Full Text Source: http://hh.diva-portal.org/smash/record.jsf?pid=diva2:574587

INTEGRATED LINEAR/NON-LINEAR HYBRID PROCESS CONTROLLER

CATEGORY: INSTRUMENTATION
PATENT
INTEGRATED LINEAR/NON-LINEAR HYBRID PROCESS CONTROLLER
United States Patent Application 20130030554
Inventors:
Macarthur, Ward (SCOTTSDALE, AZ, US)
Srinivasan, Ranganathan (BANGALORE, IN)
Hallihole, Sriram (BANGALORE, IN)
Gundappa, Madhukar Madhavamurthy (BANGALORE, IN)
Dave, Sanjay Kantilal (BANGALORE, IN)
Gaikwad, Sujit (GLENDALE, AZ, US)
Dash, Sachi (SCOTTSDALE, AZ, US)
Application Number:
13/192233
Publication Date:
01/31/2013
Assignee:
HONEYWELL INTERNATIONAL INC. (Morristown, NJ, US)
Abstract:
A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.
FIELD

Disclosed embodiments relate to feedback control systems, more specifically to methods and systems for process control using model predictive controllers.

BACKGROUND

Processing facilities, such as manufacturing plants, chemical plants and oil refineries, are typically managed using process control systems. Valves, pumps, motors, heating/cooling devices, and other industrial equipment typically perform actions needed to process materials in the processing facilities. Among other functions, the process control systems often manage the use of the industrial equipment in the processing facilities.

In conventional process control systems, controllers are often used to control the operation of the industrial equipment in the processing facilities. The controllers can typically monitor the operation of the industrial equipment, provide control signals to the industrial equipment, and/or generate alarms when malfunctions are detected. Process control systems typically include one or more process controllers and input/output (I/O) devices communicatively coupled to at least one workstation and to one or more field devices, such as through analog and/or digital buses. The field devices can include sensors (e.g., temperature, pressure and flow rate sensors), as well as other passive and/or active devices. The process controllers can receive process information, such as field measurements made by the field devices, in order to implement a control routine. Control signals can then be generated and sent to the industrial equipment to control the operation of the process.

Advanced controllers often use model-based control techniques to control the operation of the industrial equipment. Model-based control techniques typically involve using an empirical model to analyze input data, where the model identifies how the industrial equipment should be controlled based on the input data being received.

Model predictive controllers (MPCs) rely on dynamic models of the process, most often linear empirical models obtained by system identification. The models are used to predict the behavior of dependent variables (e.g. outputs) of a dynamic system with respect to changes in the process independent variables (e.g. inputs). In chemical processes, independent variables are most often setpoints of regulatory controllers that govern valve movement (e.g., valve positioners with or without flow, temperature or pressure controller cascades), while dependent variables are most often constraints in the process (e.g., product purity, equipment safe operating limits). The MPC uses the models and current plant measurements to calculate future moves in the independent variables that will result in operation that attempts to satisfy all independent and dependent variable constraints. The MPC then sends this set of independent variable to move to the corresponding regulatory controller setpoints to be implemented in the process.

In certain control systems, a difficulty may arise in operating different processes with characteristically different operating regimes. For example, some manufacturing processes, such as multi-variable chemical processes, may require control of both linear processes and non-linear processes simultaneously or successively for needed process control. Conventional controllers utilize separate linear and non-linear MPCs to handle each individual task.

SUMMARY

Disclosed embodiments recognize that conventional model predictive controllers (MPCs) that include separate non-linear and linear controllers reduce efficiency of the overall control process by increasing switchover times, and can absorb more of the user's time. Furthermore, the switchover may not be seamless which may cause a brief loss in control which can affect the integrity of the process, such as leading to one or more of production loss, out of specification product, poor quality product, and increased wear and tear on the equipment at the processing facility.

Disclosed embodiments solve the problems of conventional MPCs that include separate non-linear (NL) and linear controllers by instead providing an integrated hybrid MPC that includes both linear models and NL models. Such integrated hybrid MPCs allow switching based on the current mode or regime of operation of a physical process, such as a manufacturing process run by a processing facility or plant. The switching between linear and NL control can be a seamless bump-free switch because future process parameter predictions can be simultaneously available from both the linear model and the NL model, which facilitates smooth operation of plant or other physical system, such as during grade transitions as well as at grade operations.
Free Full Text Source: http://www.freepatentsonline.com/y2013/0030554.html

Optimal Alarm Signal Processing: Filter Design and Performance Analysis

CATEGORY: INSTRUMENTATION
IEEE Transactions on Automation Science and Engineering, Volume: PP  , Issue: 99, Page(s): 1 - 6, January 2013
Optimal Alarm Signal Processing: Filter Design and Performance Analysis
Cheng, Y., Izadi, I.; Chen, T.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
Abstract
Accuracy and efficiency of alarm systems are of crucial for the safe operation of industrial processes. Accuracy is measured by false and missed alarm rates; while efficiency relates to the detection delay and complexity of the technique used. Moving average filters are often employed in industry for improved alarm accuracy.
Authors examine the following two problems: First, given both normal and abnormal statistic distributions, how best to design an optimal alarm filter for best alarm accuracy, minimizing a weighted sum of false and missed alarm rates; and, second, how to determine in which cases moving average filters are optimal?
Full Text Source (Subscription or Fee): http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6414609&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6414609

A model-based approach for data integration to improve maintenance management by mixed reality

CATEGORY: INSTRUMENTATION
Computers in Industry, Available online 28 February 2013,
A model-based approach for data integration to improve maintenance management by mixed reality
Danúbia Bueno Espíndola a, Luca Fumagalli b, Marco Garetti b, Carlos E. Pereira c, Silvia S.C. Botelho a, Renato Ventura Henriques c
a Center of Computational Sciences, Federal University of Rio Grande – FURG, Rio Grande, RS, Brazil
b Dipartimento di Ingegneria Gestionale – POLIMI (Politecnico di Milano), Milano, Italy
c Department of Electrical Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
Abstract
Facilitating interaction with maintenance systems through intuitive interfaces is a competitive advantage. Authors present the CARMMI approach, which integrates information coming from CAx tools, mixed/augmented reality tools and embedded intelligent maintenance systems.
CARMMI provides support to operators/technicians during maintenance tasks through mixed reality, providing easier access to, understanding of, and comprehension of information from a variety of systems. Information about where, when and which data will be presented in interface are defined by CARMMI.  Three test cases that were performed using the proposed concepts and infrastructure illustrate the concept.
Full Text Source (Subscription or Fee): http://www.sciencedirect.com/science/article/pii/S0166361513000043

Ultra-wide to mid-wide angle 3X zoom and focus adjustable lens design for industrial video endoscope

CATEGORY: INSTRUMENTATION
Proc. SPIE 8557, Optical Design and Testing V, 85570P (November 26, 2012); doi:10.1117/12.999432
Ultra-wide to mid-wide angle 3X zoom and focus adjustable lens design for industrial video endoscope
Dongmin Yang
GE Inspection Technologies (United States)
Abstract
High working temperatures plus stringent design requirements pose major challenges in optical zoom lens design for industrial video endoscope. Industrial video endoscope with optical zoom capability is in high market demand.  Yet to date, no such product has been commercialized. Once the obstacles to commercialization are overcome, the technology will provide major benefits to customers in improvement of remote visual inspection work quality and productivity.
Author presents a 3X continuous optical zoom lens design with short focal length is presented in this paper. It is possible to change Field of View from ultra-wide angle to mid-wide angle. Focus distance change is from infinity to as close as 5mm. The whole lens train has a maximum diameter of 3.0mm, and overall length of 8.7mm, which makes it practical to be integrated into a 6mm industrial video endoscope. Image quality in terms of contrast and resolution exceeds today’s existing commercial 6mm industrial video endoscopes.
Full Text Source (Subscription or Fee): http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1457374

System and Method of Determining Gas Detector Information and Status via RFID Tags

CATEGORY: INSTRUMENTATION
PATENT
System and Method of Determining Gas Detector Information and Status via RFID Tags
United States Patent Application 20120007736
Inventors:
Worthington, Stephen D. (Calgary, CA)
Stinson, Sean Everett (Calgary, CA)
Application Number: 12/831908
Publication Date: 01/12/2012
Assignee: Honeywell International Inc. (Morristown, NJ, US)
Abstract:
In large systems of ambient condition detectors the respective detectors can each include an RFID-type tag or integrated circuit. The tag can transmit detector identification information and status information wirelessly to a displaced receiver. Receivers can be installed in docking/test stations as well as in portable units which can be carried by an individual entering, or, moving through a region being monitored by the detectors
FIELD
The invention pertains to systems that need large numbers of gas or smoke detectors to monitor an industrial or commercial environment. More particularly, the invention pertains to detecting the status of such detectors in the context of managing large industrial environments such as refineries.
BACKGROUND
Large numbers of gas detectors are frequently required during events such a refinery shutdowns and there are several companies that provide rental instruments as a service. In the event of large refinery shutdowns, several thousand rental gas detectors may be required. In these situations, both the rental company and the company using the detectors have to manage a large number of instruments. They must determine ownership of instruments as well as verify the operational status of each instrument.
While every instrument has a unique serial number, it can be difficult to read and the operational status of the instrument (i.e. is the calibration and bump check status up to date). It is desirable to have some means of quickly and reliably reading large numbers of instrument serial numbers as well as the associated operational status. It is also desirable to collect this information without having to remove detectors from packaging or shipping containers.
Free Full Text Source: http://www.freepatentsonline.com/y2012/0007736.html

Anomaly detection and prediction of sensors faults in a refinery using data mining techniques and fuzzy logic

Scientific Research and Essays Vol. 6(27), pp. 5685-5695, 16 November, 2011
Anomaly detection and prediction of sensors faults in a refinery using data mining techniques and fuzzy logic
Mahmoud Reza Saybani*, Teh Ying Wah, Amineh Amini and Saeed Reza Aghabozorgi Sahaf Yazdi
saybani@gmail.com
saybani@siswa.um.edu.my
Department of Information Science, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia
Abstract
Refineries use many sensors to monitor and control the process of refining.  It is critical, therefore, to detect any sensor faults or anomalies as early as possible, and to be able to replace or repair a sensor well in advance of any fault.  Authors present a method for detecting anomalies in a sensor’s data, as well as to predict the next occurance of a sensor failure.
Data mining techniques to detect anomalies in sensor data and predict the occurrence of next faulty event were introduced. Researchers used MATLAB’s fuzzy logic toolbox tools to find clusters.  The MATLAB toolbox uses subtractive fuzzy clustering algorithm and generates a model, a Sugeno-type fuzzy inference system. The same toolbox was used to evaluate the model with promising results.
Free Full Text Source: http://www.academicjournals.org/SRE/PDF/pdf2011/16Nov/Saybani%20et%20al.pdf