An Online Fault Diagnosis Strategy for Full Operating Cycles of

Article pubs.acs.org/IECR

An Online Fault Diagnosis Strategy for Full Operating Cycles of Chemical Processes Jinsong Zhao,† Yidan Shu,† Jianfeng Zhu,† and Yiyang Dai*,‡ †

State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, China CNPC Research Institute of Safety & Environment Technology

‡

ABSTRACT: Online fault diagnosis is one of the most important methods to ensure stability and safety in many chemical processes. In this work, a lab-scale distillation process is designed and built for fault diagnosis study, and the online fault diagnosis system (OFDS) is developed with a distributed control system (DCS) system and a real-time database. Artificial neural networks (ANNs) are used for startup state judgment and for fault detection in the steady state, while the dynamic artificial immune system (DAIS) is used for fault detection in the startup phase and for fault identification in both the startup phase and the steady state. The results of case studies clearly illustrate that the developed system is efficient in online fault diagnosis of distillation processes during the full operating cycle, especially when the number of historical fault samples is limited. The self-learning ability of the methods ensures that the system can remember and diagnose new faults, and the friendly interface of OFDS can show the current condition of the process to operators and get feedback from the operators for online learning.

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INTRODUCTION With the development of modern technologies, most chemical processes have become more and more complex and larger in scale, and some automatic manufacturing systems, such as DCS and MES (manufacturing execution system), are used widely around most chemical plants. At the same time, the number of operators has been reduced a lot. As a result, most of the operators have to deal with several process units, and simple alarming of abnormal values of process variables is insufficient for process monitoring by a less experienced operator. Hence, there is a need for a fault diagnosis system, which is capable of analyzing online data and identifying probable abnormal situations, so that operators can see the probable faults on a friendly interface of the system through simple and clear messages. According to the instruction given by the fault diagnosis system, operators can take appropriate countermeasures to deal with the abnormal situations. Hence, a potential accident may be avoided, and the process safety and stability of chemical plants may be greatly improved. During the past four decades, various process models and algorithms for fault diagnosis have been developed. In general, they can be classified as quantitative model based methods, qualitative model based methods, and process history based methods.1−3 However, still very few online fault diagnosis systems have been widely applied in chemical processes so far. As for online applications, the fault diagnosis system should at least meet the following requirements: (1) The algorithm speed should be fast enough to detect the fault before reading the next online data, and a potential root cause should be identified in a short time after a fault is introduced. (2) The online system should have a comprehensive framework, in order to read in online data of a large number of variables immediately and show the diagnosis result to operators with a simple message. © XXXX American Chemical Society

(3) The online system should have the self-learning capability to adapt to new conditions. Due to the uncertainties existing in the complex chemical process, very few devices are able to maintain a steady state in the entire service life cycle. To deal with the uncertainties of chemical processes, the function of selfadaption is necessary. (4) The online fault diagnosis system must have a friendly man−machine interface, so that when a wrong diagnosis occurs or an unknown fault is detected by the system, simple actions could be taken to correct the system and improve the fault diagnosis efficiency. Most of the latest studies have focused on the algorithms of online fault diagnosis of chemical processes. ANNs,4−6 principal component analysis (PCA),7−9 dynamic locus analysis (DLA),10 Petri nets,11,12 and many other methods are used in online fault diagnosis studies. However, every algorithm has its own advantages and limits. Many experts attempt to use multiple methods in one system for fault diagnosis of complex chemical processes. Li et al.13 proposed a nonlinear dynamic principal component analysis (ND-PCA) approach based on dynamic PCA and the sigmoid basis function feed forward neural network (SBFN) and illustrated it on the Tennessee Eastman benchmark process as a case study while noises were added on sensor readings. Ruiz et al.14 proposed a fault diagnosis system for batch chemical plants consisting of an ANNs structure supplemented with a knowledge based expert system (KBES) in a blockoriented configuration. They used a batch reactor as a case study to demonstrate the ease of implementation and its good performance by the correct identification of the faults. Kämpjärvi et al.15 developed an online fault detection and isolation system Special Issue: David Himmelblau and Gary Powers Memorial Received: March 1, 2013 Revised: June 19, 2013 Accepted: July 19, 2013

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dx.doi.org/10.1021/ie400660e | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

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DAIS FOR FAULT DIAGNOSIS Artificial Immune System. The immune system is a defense system that has evolved to protect its host from pathogens (harmful micro-organisms such as bacteria and viruses).27 It can protect the body against infections and has a very distributed and adaptive novel pattern recognition mechanism. Once pathogens enter the organism by breaching the physical barriers including skin, lungs, respiratory tract, et al. and enter the organism, the innate immune system provides an immediate but nonspecific response. Phagocytes will attack the foreign invaders at first. They can eat the pathogens and destroy them immediately. If the innate response fails to destroy the pathogens, the adaptive immune system is activated by the innate response. Some portions of the pathogen, called antigens, are recognized by the adaptive immune system. Specific antibodies are generated by lymphocytes to defend the body against antigens. Adaptive immunity responds to a challenge with a high degree of specificity, as well as the remarkable property of “memory.” If the same antigen is found some time in the future, a memory response will be activated: the immune response to the second challenge occurs more quickly than the first, is stronger, and is often more effective in neutralizing and clearing the pathogen. Since many properties of the immune system are of great interest for engineers, the artificial immune system (AIS) was proposed over 20 years ago to solve engineering problems. In general, AIS is a diverse area of research that attempts to bridge the gap between immunology and engineering, and it is developed through the application of techniques such as mathematical and computational modeling of immunology, abstraction from those models into algorithm (and system) design, and implementation in the context of engineering.28 In 1990, Ishida used the principle of an immune system to deal with the problem of fault diagnosis of sensor networks, which was the first application of the AIS to the engineering field.29 Later, Forrest et al. used an immune system for computer security and virus detection.30 So far, AIS has been widely used in various diverse fields. Most research can be found in the reviews of AIS by Timmis.31 Recently, some research has used an artificial immune system for fault diagnosis of chemical and petrol chemical processes, such as in oil wells,32 the Tennessee Eastman (TE) process,33 the CSTR process,34 and batch chemical processes.35 Negative selection and clonal selection are the most popular immune algorithms used in fault diagnosis. The starting point of the negative selection algorithm is to produce a set of self-strings, S, that define the normal state of the system. The task then is to generate a set of detectors, R, that only bind the complement of S. These detectors can then be applied to new data in order to classify them as being self or nonself.30 However, in complex chemical processes, there are too many variables, and the value of them may differ very much. A detector which can bind the entire complement of self-strings is too hard to get. So we think a clonal selection algorithm is more suitable for chemical processes. A clonal selection algorithm is different from a negative selection algorithm. It uses the idea of memory cells to retain good solutions to the problem being solved. Initial antibodies are generated from the historical data.36 Then, some of the initial antibodies are randomly selected and cloned with mutation to generate a large number of memory antibodies. Only those memory antibodies that match the initial antibodies well enough can be retained. New data can be considered as antigens, which

in an ethylene cracking process consisting of a combination of PCA and two ANNs. Through different kinds of methods, some integrated systems have been developed for online fault diagnosis. Ferrer-Nadal et al.17 envisaged an integrated supervisory framework for more robust online optimization and exception handling. The system was implemented in a debutanizer column, and decision making was enhanced by the information provided, allowing plant operators a better understanding of what was happening in the plant at every moment. Power and Bahri16 proposed a two-step supervisory fault detection and diagnosis framework implemented in G2. A Petri net was used for fault detection, and G2 NOL Radial Basis Function Neural Networks were used for fault diagnosis and was applied to a Pilot plant, which was a representation of a Bayer process. Maurya et al.18 proposed a framework for online fault diagnosis and developed a prototype in the Matlab programming environment. The utility of the framework was illustrated through fault diagnosis of the Tennessee Eastman benchmark process. However, most of these frameworks did not cover the full operating cycle from startup to steady state operation. The structure of this article is as follows. Initially, a brief overview of ANNs for fault diagnosis is provided. After that, AIS is introduced, and the algorithm used for fault diagnosis is proposed. This is followed by the framework of OFDS. Then, we illustrate the OFDS using an online experiment on a pilot-scale distillation process both in the startup phase and in the steady state. Finally, conclusions of the work are presented.

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ANNS FOR FAULT DIAGNOSIS

Since the late Prof. Himmelblau together with Josiha Hoskins published the first paper in the area of chemical engineering describing the potential of ANNs in 1988,5 ANN has become one of the most popular ones among fault diagnosis methods. Most of the literature concerning fault diagnosis and neural networks has focused on fault detection based on steady state data. Venkatasubramanian et al.23 used neural networks for fault detection and diagnosis of faults in a continuously stirred tank reactor (CSTR) and a distillation column. Back propagation networks (BPNs) using sigmoidal functions in the first layer were applied in their work. Leonard and Kramer later24 proposed radial basis function networks (RBFNs) for classifying process faults which have then been widely used and improved for fault diagnosis. Zhao et al.25 proposed a wavelet-sigmoid basis function neural networks (WSBFNs) model for dynamic fault diagnosis. Its application to a dynamic hydrocracking process illustrated that WSBFN owned an advantage in the dynamic process compared with traditional neural networks. Recently, more and more researchers have begun to study the adaptation algorithm for fault diagnosis in industrial applications. Barakat et al.26 introduced Self Adaptive Growing Neural Network (SAGNN) for fault diagnosis based on automatic structure building and a parameter tuning procedure. Due to its high calculation speed provided by massive parallelism, a greater degree of robustness, and the ability to adapt and continue learning to improve performance,24 RBFN is applied in our proposed OFDS to identify the turning point from the startup stage to the steady operation state and the turning point from the normal steady state to an abnormal state. B



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will be matched with memory antibodies to classify them as being self or nonself. Dynamic Artificial Immune System. No matter what algorithm is used in AIS, antigens and antibodies are usually represented by vectors of data from a sample at a certain time. However, once a fault is introduced into the process, data changes will be reflected in trends, and antigens and antibodies which are simply represented by vectors of data do not contain trend information. This is not conducive to the diagnosis of fault type or early fault detection. To deal with this problem, Dai and Zhao have proposed a dynamic AIS (DAIS),35 in which antibodies and antigens are represented by matrices of timesampled data, instead of vectors of data. In previous research, antibodies and antigens are generated by normalized online data. However in industrial processes, the variables in the normal state may vary widely. So the difference between antigen and antibody may be too great if they are in different normal states, even if they are of the same type. To deal with this problem, we use the data deviation from the normal state to generate antibodies and antigens. Figure 1 shows an antibody for one type of fault. The antibody in Figure 1 contains a set of time-sampled data with six variables and 20 samples.

d ( i , j) =

∑ (ωc|R(j , c) − T(i , c)|) c=1

(3)

where i and j denote the sample times in T and R, respectively, c denotes the variables of R and T, and ωc denotes the nonnegative weight of variable c. Using the Itakura constraint,33 another matrix, D, representing the dissimilarity distance between the two subsequences is then formulated: D(i , j) = ⎧ D(i − 1, j) + d(i , j) or [∞ , if condition A]⎫ ⎪ ⎪ ⎪ ⎪ ⎬ min⎨ D(i − 1, j − 1) + d(i , j) ⎪ ⎪ ⎪ ⎪ D(i − 1, j − 2) + d(i , j) ⎭ ⎩ (4)

where D(1,j) = d(1,j) and condition A indicates that the predecessor of point (i−1, j) is the point (i − 2, j). Hence, the point j* in the last column of the matrix D corresponding to the minimum distance between R and T can be determined: j* = arg minj{D(m , j)}, j ∈ [1, n]

(5)

The sequence F* that matches between R and T is described as F * = {(1, l), (2, l + 1), ..., (m , j(m))}

(6)

where j(m) = j*. Following this sequence F*, a difference matrix φ can be calculated by φ(i , c) = R(j(i), c) − T (i , c)

(7)

Finally, the normalized difference η between R and T is calculated as m

η (T , R ) =

In our proposed DAIS, dynamic time warping (DTW) is introduced to calculate the difference between antigen and antibody. DTW is a flexible method for comparing two dynamic patterns that may not be perfectly aligned and are characterized by similar, but possibly expanded or contracted, temporal correlations.37 It was first used in the area of speech recognition. Kassidas et al. first used it for fault detection and diagnosis of chemical process.38 When a DTW algorithm is used for fault diagnosis with DAIS, R and T denote antibody and antigen with dimensions r × n and t × n, where n is the number of variables and r and t are the lengths of the data for the variables in each matrix, respectively. The main goal of DTW is to find a minimum sequence F of K points on a t × r grid: (1)

c(k) = [i(k), j(k)]

(2)

m

(8)

Besides the difference calculation, the cloning algorithm is also proposed, which is different from the traditional clonal selection algorithm. In our study, the original antibody is composed of a deviation matrix of the historical data after the time that the fault was introduced:

Figure 1. An antibody of the artificial immune system.

F = {c(1), c(2), ..., c(k), ..., c(K )}

∑i = 1 |φ(i)|

m×n Abfault = [v1 − v1(1), v2 − v2(1), ..., vk − vk(1), ..., vn

− vn(1)]

(9)

where vi indicates the data of variable i and vi(1) represents the value of variable i at the time that the fault was introduced. If there is only one original antibody X0 of one type, X* indicates the antibody cloned from X0, Xn indicates a section randomly cut from the historical normal data with the same size of X0, and a indicates a random number from 0.5 and 2 in this paper. The new antibody X* can be cloned with mutation by X * = aX 0

(10)

If there are more than two original antibodies in one type, take two antibodies randomly from the same type library. Let X1 and X2 indicate the two original antibodies, and calculate the difference matrixes φ* between the two antibodies by using eq 8. Let X* indicate an antibody cloned from X1 and X2 with mutation. a indicates a random number between 0.5 and 2, and b

In carrying out the DTW, a matrix, d, representing the Euclidean distance between R and T is constructed at first: C



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Figure 2. The framework of OFDS.

The OFDS system is first initialized with historical data. The startup state judgment RBFN is trained with normal data in both the startup state and steady state, while the steady state fault detection RBFN is trained with both normal and faulty data in the steady state. On the other hand, the startup antibody library and steady state fault antibody library are built with the startup data and steady state faulty data separately. During online application, OFDS communicates immediately with a real-time database. The online data are monitored by the startup state judgment RBFN to judge whether the process is in the startup state or in the steady state. If the process is in the startup state, DAIS will conduct fault detection and diagnosis using the startup antibody library. If the process is in the steady state, steady state fault detection RBFN will conduct fault detection. If faults are detected by RBFN, DAIS will identify what fault has occurred using the steady state fault antibody library. If the diagnosis results are found to be incorrect, or no faults can be diagnosed by OFDS, operators can manually input the diagnosed fault type. With the manual input, OFDS will conduct self-learning through updating the corresponding antibody library or construct a new antibody library with the data after the fault is detected. Online Fault Detection and Identification. System Initialization. Before online fault diagnosis, neural networks need to be trained, and the antibody libraries need to be constructed. The ANNs used in OFDS are RBFNs. A radial basis function is a function which is symmetrical about a given mean or center point in a multidimensional space. According to previous research, RBFNs are suitable for pattern recognition problems and have been widely used in engineering applications for their ability of self-adaptation to online learning without dramatically affecting previous learning.34 As a result, the networks can be retrained by new data if a mistake is made or a new fault occurs. The inputs of the networks include all the variables of historical

indicates a random number between −1 and +1 in this paper. The new antibody X* can be cloned with mutation by X * = aX1 + bφ*

(11)

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ONLINE FAULT DIAGNOSIS SYSTEM FOR FULL OPERATING CYCLE DAIS has an excellent performance for fault diagnosis because of its strong adaptation capability and independency of the number of training samples. However, a process is in the normal operation state most of the time. The computation load of DAIS is so heavy that it is not resource-efficient to use it for fault detection during the normal operation state. On the other hand, since it is easy to collect abundant normal samples under steady states, ANN can be trained to achieve satisfactory accuracy in determining whether the startup is completed and whether the steady state is normal or not. To make full use of the advantages of AIS and ANN, our proposed OFDS for the full operating cycle combines ANN and DAIS in an integrated framework. During startup, DAIS is used to detect and identify faults. ANN is used for detecting the turning point for fault detection, and AIS is used for fault diagnosis. In the steady state, ANN is used for fault detection, and AIS is used for fault diagnosis. The ANN used in this work is RBFN. Online Fault Diagnosis System Framework. The proposed OFDS is developed for a distillation process, which was designed and installed to study industrial fault diagnosis. A DCS system is used to ensure the stable operation of the distillation process and get the process data. A real-time database, Aspen Info Plus 2.1, is used to collect and store large volumes of process data from DCS for online analysis. The framework of OFDS is shown in Figure 2. D



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wherein Agi is the time series of the deviation of the ith variable compared with the reference value of the normal state. When DAIS reads the next sampling point to generate new antigens, T changes with the sampling point changing. But T0 only depends on the moment when faults are detected and will not change. So the elements in the first row of Ag will not stay zero. The differences between the antigen and all the antibodies in all of the fault antibody libraries are calculated. Denote the differences by ηk = [ηk(1), ηk(2), ..., ηk(n)], and ηk(i) indicates the difference between the antigen and antibody i from the fault k antibody library (k = 1, 2, ...., N, and N represents the number of known fault types). If min(ηk) δ3. I



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Figure 10. Interface of OFDS under normal conditions.

Figure 11. Interface of OFDS when a fault is detected.

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CONCLUSIONS Online fault diagnosis is one of the most important methods to ensure stability and safety in many chemical processes. This article developed an online fault diagnosis system for a lab-scale distillation process. Due to its high computation speed, robustness, and adaptive ability, RBFN is used for steady state fault detection and startup state judgment, while DAIS is used for fault diagnosis because of its strong adaptation capability and

independency on the number of training samples. Our proposed OFDS combines RBFNs and DAIS in an integrated system. Online data can be read in from a real-time database which is connected with a DCS system. Neural networks and antibody libraries are stored in a relational database, for both fault detection and identification. A graphical user-friendly interface is developed to show the diagnosed results and get inputs from operators for online learning. According to the result of manual J



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Figure 12. Diagnosed window when a fault was diagnosed.

Figure 13. Diagnosed window when a new fault is input.

fault types will be shown in a friendly interface for mitigating and/or removing the faults. Unknown faults can also be detected by the system, and a user interface has been designed to allow operators to input the results of manual diagnosis. These manual diagnosis results together with the corresponding fault data are stored in antibody libraries. When the same fault occurs next time, the system can diagnose the fault and show the diagnosis results to operators immediately.

diagnosis, neural networks can be retrained, and antibody libraries can be updated. The performance of OFDS was tested with online experiments. The results of case studies clearly illustrate that the developed system is efficient in online fault detection and diagnosis of distillation processes for both startup and the steady state, especially when the number of historical samples is limited. Known faults can be diagnosed by the system, and the probable K



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Figure 14. Interface of OFDS when a new fault occurred again.

Although our proposed OFDS showed its effectiveness in a lab-scale process, whether the faults can be diagnosed on time will be a major concern when it is applied online in a larger scale process. Future work will be focused on a faster algorithm to calculate affinities between antibodies and antigens. In addition, lengths of antibodies, sizes of antibody libraries, and values of affinity threshold need to be further optimized.

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RBFN = radial basis function network SAGNN = self-adaptive growing neural network SBFN = sigmoid basis function feed forward neural network WSBFN = wavelet sigmoid basis function neural network

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +86 10 80169740. Fax: +86 10 80169764. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to gratefully acknowledge financial support from the National Basic Research Program of China (973 Program) (Grant No. 2012CB720500) and the National High-Tech R&D Program of China (863 Program) (No. 2013AA040702).

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ACRONYMS AIS = artificial immune system ANN = artificial neural network BPN = back-propagation network CSTR = continuously stirred tank reactor DAIS = dynamic artificial immune system DCS = distributed control system DLA = dynamic locus analysis DTW = dynamic time warping KBES = knowledge based expert system MES = manufacturing execution system ND-PCA = nonlinear dynamic PCA OFDS = online fault diagnosis system PCA = principal component analysis L



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An Online Fault Diagnosis Strategy for Full Operating Cycles of

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