Automatic Fault Isolation by Cultural Algorithms With ... - IEEE Xplore

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 56, NO. 5, OCTOBER 2007

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Automatic Fault Isolation by Cultural Algorithms With Differential Influence Pasquale Arpaia, Giuseppe Lucariello, and Antonio Zanesco

Abstract—An evolutionary algorithm with a cultural mechanism of evolution influence for effectiveness and efficiency higher than classical genetic algorithms is proposed for industrial fault isolation. Moreover, the evolution influence is based on a differential concept in order to move toward better zones of the solution space by sensing the fitness gradient. The proposed cultural algorithm is designed in order to be portable and easily configurable in different diagnostic applications. On-field results of an industrial application to motor-vehicle fleet remote monitoring and automatic fault isolation of vehicle wear, operating danger, and fraud in a company that transports dangerous goods are shown. Index Terms—Artificial intelligence, fault diagnosis, fault isolation, genetic algorithms (GAs), road transportation.

I. I NTRODUCTION

I

N RECENT years, advanced remote monitoring systems for industrial applications are increasingly more oriented towards automatic diagnostic aims. In multiple fault isolation, several alarms may occur simultaneously, and a corresponding set of faults that best explain the abnormal behaviors have to be identified with the corresponding occurrence probabilities. An evolutionary approach (such as genetic algorithms (GAs) [1]) showed satisfying capabilities for scanning large solution spaces [2], [3]; however, an intrinsic resource waste, particularly in real parameter optimization, such as in the case of industrial fault isolation, was highlighted [4]. In other recent applications, this problem has been faced by cultural algorithms (CAs): In this framework, culture is intended to be the vehicle to store information that will be made available to all population members during evolution [3], [5]–[9]. A specific mechanism of evolution pressure, which is called belief space, is analogously exploited, as in culture evolution (“cultures evolve faster than species”). The characteristics of best individuals update the knowledge in the belief space by evolving the information for a faster and better solution search. The updated knowledge is transferred to new individuals. Thus, they can also access information that has not been explored by their ancestors directly. In this way, the intrinsic resource waste of genetic evolution is avoided by driving evolution suitably through such a cultural mechanism. However, for real parameter optimization, such as Manuscript received June 30, 2005; revised May 30, 2007. This work was supported by the Italian Local Government of Regione Campania (POR Measure 3.17) under the Project PROMADAT. The authors are with the Department of Engineering, University of Sannio, 82100 Benevento, Italy (e-mail: [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIM.2007.903604

in the case of occurrence probability for multifault isolation in an industrial environment, state-of-the-art CAs exhibit drawbacks related to 1) the lack of a direction track toward the best solution [10] and 2) the development burden (time and cost) for different applications. In this paper, a multiple fault isolation approach, which is based on a CA with improved effectiveness and efficiency performance, is proposed. A differential evolution influence [12], [13], with main design emphasis on real parameter optimization, is exploited in order to widely track optimum solutions. Moreover, a specific architecture allows immediate portability to different diagnostic applications. Experimental results, which are related to the on-field implementation of the proposed CA to automatic fault isolation of a transport company dealing with dangerous goods, are discussed. II. P ROPOSED A PPROACH In the following, 1) the basic ideas and 2) the architecture of the proposed automatic system for fault isolation are described. A. Basic Ideas The main basic ideas of the proposed approach are given. • Fault isolation by CA (Fig. 1) is used to improve search performance by providing genetic evolution by a suitable pressure. With respect to other evolutionary algorithms, CAs extract and exploit information on the problem domain during evolution. With this aim, the following two main components are used: 1) the population space and 2) the belief space [2]. The population space consists of a set of possible solutions to the problem, which are modeled by a population-based technique, e.g., a GA. The belief space is the information repository where evolving individuals store their experiences for other individuals in order to learn them indirectly. In this way, the information acquired by an individual can be shared with the entire population, unlike most evolutionary techniques, where the information can only be shared with the individual’s offspring. Both spaces (i.e., population and belief) are linked through a communication protocol, stating both the rules about the individuals contributing to the belief space with their experiences (Accept function in Fig. 1) and the way the belief space can influence new individuals (Influence function in Fig. 1). In this way, the following twofold heritages are made possible: 1) by means of accept and influence functions and 2) by means of each individual’s own experience. The belief space was organized in three knowledge domains as follows,

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Fig. 1. Proposed CA for fault isolation.

each one with its specific function of evolution influence: 1) the situational domain to orient the evolution in the best direction; 2) the topographical domain to address the evolution in suitable regions of the search space; and 3) the historical domain to track the memory of the evolution and avoid local solutions. • Differential influence of the belief space on the population evolution [11]–[13] (Fig. 2) is used to 1) force the evolution to move toward better zones of the solution space by estimating the fitness gradient in a zone, rather in a point, as the difference between two randomly chosen individuals of the population (Fig. 3); 2) improve efficiency [11] in the self-adaptation of mutation by exploiting the best characteristics of the previous evolution cycle contained in the knowledge domains of the belief space; and 3) enhance search in a real domain (infinitely dense in itself) by sensing the shape of the domain through the gradient. In particular, according to the explicit suggestion [12] of the author of the differential influence, among several proposals [13]–[16], the algorithm DE/rand/1/bin was selected for its easy application. • Specific design for portability is used in order to allow the fault isolation knowledge of the application field to be easily introduced by the technical user; the CA has a configuration module (Fig. 1) driving the user to define the faults, the corresponding anomalies in predefined signals

(e.g., anomalous residuals of monitored quantities), and the related occurrence probabilities. B. Architecture On this basis, the proposed architecture includes the following (Fig. 4): • data acquisition section: a distributed system for measurement and local data preprocessing based on suitable monitoring units (e.g., sensors, interfaces to industrial processes, and so on); • data transmission section: a communication system between monitoring units and base station (central), which is based on a server–client architecture exploiting Transmission Control Protocol–Internet Protocol and physical wireless channel Global System for Mobile Communications (GSM)–General Packet Radio Service (GPRS); • section of fault detection and isolation: an automatic system devoted to reveal and identify faults in the monitored system, which is based mainly on the aforementioned CA; • configuration module: a user interface aimed at driving a user, which is skilled in a specific field, in customizing the CA by entering technical knowledge on faults and corresponding anomalies in monitored signals in an informal way.

ARPAIA et al.: AUTOMATIC FAULT ISOLATION BY CULTURAL ALGORITHMS WITH DIFFERENTIAL INFLUENCE

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of individuals xr1 and xr2 define the local gradient of the fitness (Fig. 3), whereas xr3 is the starting individual that finds a new candidate solution xj with the same gradient. The individual xj is mutated in the new individual xj by determining a random value irand , pointing out the chromosome to be changed (Fig. 2). Their chromosomes are determined as If rand < CR

or

i = irand

x i, j = xi,r3 + F ∗ (xi,r1 − xi,r2 ) Else, xi,j = xi,j where rand is a random number, and CR and F are parameters defined by the user in order to act on the mutation randomness and on the distance from r3 , respectively. The parameters rand, CR, and F range between 0.0 and 1.0. If the mutated individual xj has a fitness better than the one of xj , the population is updated by substituting xj to xj . The algorithm ends when all the individuals are processed. B. Design of the CA

Fig. 2.

Flowchart of the differential evolution algorithm.

Fig. 3.

Individuals in the differential evolution.

III. F AULT I SOLATION BY CA S In the following, 1) the differential evolution algorithm and 2) the design of the CA of the fault detection and isolation section are detailed. A. Differential Evolution Algorithm The algorithm of the differential evolution is shown in Fig. 2. An initial population of fault configurations is randomly generated on the basis of the information entered through the configuration module. For each individual xj of the population to be evolved (Fig. 3), three distinct integers r1 , r2 , and r3 , which are different from j, are randomly generated, in order to search in another region of the solution space. The first couple

The proposed CA for fault isolation consists of three main parts, namely 1) the configuration module; 2) the population space; and 3) the belief space (Fig. 1). The CA knowledge is coded by the configuration module as a matrix (causal strength matrix CS in Fig. 1): Each row corresponds to a possible anomalous signal and each column to a fault. In this way, each matrix element defines the conditioned probability that, given the fault of the corresponding column, the signal anomaly related to its row would arise. The matrix CS encodes the fault diagnosis knowledge of human experts for the application, which is realized by means of their suggestions, as well as statistical analysis of fault isolation history. The population space is based on a GA where each individual represents a candidate multifault configuration, i.e., a set of faults candidate for isolation. An individual is coded as an array of real numbers (chromosomes): The ith index denotes the ith fault, whereas the ith value represents the probability of occurrence of the ith fault. In the search space, each chromosome of an individual represents a single coordinate of a solution candidate. In this way, the proposed method is intrinsically devoted to multifault isolation. (It worth noting that the CA output can also be composed of several faults with 100% of probability occurrence, if their occurrence is considered to be certain.) In particular, the final solution will diagnose the generic situation of an anomalous condition where multiple faults are occurring in the system. The search is carried out by 1) altering the chromosomes according to genetic evolution driven by a cultural mechanism and 2) assessing the quality of the current solution through a suitable quality index, i.e., the fitness function. In particular, the population evolves by means of the function Variation (Fig. 1), according to the operations of crossover, mutation, and migration, which is realized as such in the GA literature [17]–[21]. The fitness function (Performance in Fig. 1) is based on the probabilistic causal model [22] in order to solve multiple fault isolation problems effectively [22]. This approach exploits

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Fig. 4. Architecture of the proposed system.

the notion of efficiently covering a set of measurable anomalies, i.e., finding a minimal set covering or a set of faults that explains the detected out-of-bound conditions. Symbolic cause–effect inference is integrated with numeric probabilistic inference in order to overcome the difficulty in coverage and minimality. In particular, an objective function expresses the relative likelihood that a set of faults could have caused the measured anomalies. The likelihood is expressed in terms of three main factors, namely 1) the likelihood L1 that a subset of the given set of possible faults as a whole can cause the measured anomalies; 2) the likelihood L2 that a subset of the given set of possible faults as a whole does not cause anomalies that are different from the measured ones; and 3) the likelihood L3 that a very common fault contributes significantly to the overall likelihood of a diagnosis including it. With this aim, the diagnosis problem is characterized as the 4-tuple F a, A, C, A+ , where F a is a finite nonempty set of independent faults; A is a finite nonempty set of anomalies in the monitored signals (e.g., residuals); C is a relation pairing faults with related anomalies, such that (f a, a) ∈ C means that the fault f a may cause an anomaly a; and A+ is a subset of A identifying the observed anomalies. An isolation I (a subset of F a) identifies the faults that are possibly responsible for the anomalies in A+ . Isolation I is defined to cover A+ if each of the individual anomalies in A+ is associated with at least one of the faults in I, as determined by using C. Associated with each fault f aj in F a is an a priori probability pj , where 0 < pj < 1. Associated with each “causal association” in C is a causal strength cij (such that 0 < cij < 1), representing how frequently a fault f aj causes anomalies ai . The causal strength represents the conditional probability P (f aj causes ai |f aj ). An additional assumption states that no anomaly may exist in A+ unless it is actually caused by some faults in F a. Thus, |F a| are a priori probabilities, and |F aA| are causal strengths. By using these values, a formula

for calculating the “relative likelihood,” which is denoted as L(I, A+ ), of an isolation I, given observable anomalies A+ , is derived [22]. The likelihood is the product of three factors, i.e., L(I, A+ ) = L1 L2 L3 . The first factor



L1 =

1 −

ai ∈A+

n

(1) 

1 − cij · xj )

(2)

j=1

is the likelihood that faults in I cause the anomalies in A+ . For an isolation not covering A+ , L1 evaluates to 0, thus forcing L to 0. The second factor L2 =

n

(1 − cij · xj )

(3)

j=1 ai ∈(A−A+ )

is the likelihood that faults in I do not cause anomalies outside of A+ (e.g., in A − A+ ). According to [22], L2 is “a weight based on anomalies expected with I but which are actually absent.” Ideally, L2 values close to 1 are preferred. Finally, the third factor L3 =

n 1 − xi (1 − pi ) i=1

1 − pi xi

(4)

is the likelihood that a highly probable fault f aj contributes significantly in the overall likelihood of an isolation I containing f aj . After the GA evolution, individuals are classified according to their best fitness (Scoring in Fig. 1). The Accept function (Fig. 1) takes a given percentage of the best individuals in order to extract the best characteristics for the belief space. The GA evolution is restarted again after a new population, which is


Fig. 5.

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Function topographical influence it . (a) Selection of individuals. (b) Algorithm of differential influence.

suitably influenced by cultural mechanisms in the belief space, is obtained. The CA cycles end when the fitness is considered to be better than a prefixed value or when the fitness does not change significantly after a given number of cycles. The evolution of the population space is driven by the belief space of the CA. With this aim, this last part is organized in three knowledge domains (Fig. 1) as follows: 1) situational; 2) topographical; and 3) historical [2], [5]–[8]. The situational domain hosts the best individuals found in previous evolution cycles of the GA. The domain is initialized by a default arbitrary population (e.g., all null solutions). The influence function is (Fig. 1) mutates the GA population P op1 in order to obtain a new population P op2 according to the following rules: 1) two elements of P op1 are selected by means of two random numbers r1 and r2 ; 2) for each chromosome j of the ith individual, P op2(i, j) = P revInd(j) + F ∗ (P op1(r1 , j) − P op1(r2 , j)), where P revInd is the previous individual in the situational domain. The update function us compares the best individual with the current one and, if better, stores it in the domain. The topographical domain is aimed at mapping the solution search space during evolutions of the population space by recording regions (cells) with best fitness in a ranked list. The knowledge is organized as a matrix containing the chromosomes of an individual and the coordinates of the cell where the individual was found. The function topographical influence it generates a population P op2 from the current P op1, according to the algorithm of Fig. 5. In particular, an individual of P op2 is obtained by mutating three individuals of P op1, i.e., xr1 ,

xr2 , and xr3 , which are randomly selected [Fig. 5(a)]. For each of the three individuals, a chromosome in position i is randomly selected (xr1,i , xr2,i , and xr3,i ). A row of the matrix in the topographical knowledge is randomly selected, and at the position i, the bounds of the current cell [Lji and Uji in Fig. 5(a)] are determined. Then, 1) if the value xr3,i of the ith chromosome of the individual xr3 is less than Lji , the chromosome is updated as shown in Fig. 5(b), where F is an arbitrary parameter belonging to (0, 1) to suitably force the mutation once a test on the variable random rand(1) was carried out; 2) if the value belongs to the interval (Lji , Uji ), the chromosome is updated as shown in Fig. 5(b), after the aforementioned test; and 3) if it is greater than Uji , the chromosome is updated as shown in Fig. 5(b). If the chromosome should exceed the bounds 0.0 or 1.0, its value is forced to 0 or 1, respectively. If the test result is negative, the chromosome is left unvaried. For each update of the belief space, new individuals are considered according to the Accept function (Fig. 1). The cells are determined according to the k-dimensional tree [23] data structure, having, for each dimension, nodes with only two children. The root node coincides with the search space as a whole and contains the best solution found in the initial population. The remaining tree is built through the function topographical Update ut by verifying time by time if the new individual belongs to an already existing cell; in the positive case, the cell is divided. The historical domain’s aims are twofold, namely 1) to identify position patterns of local optimal solutions in order to avoid them and 2) to record best individuals in the past GA

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Fig. 6. Implemented system for automatic monitoring and fault isolation of the transport of dangerous goods.

evolution cycles in order to recover previous good candidate solutions. In the former case, if a solution remains as the best one for a given number of GA generations, the CA could be trapped in a local optimum, and this solution is stored in order to avoid it in the future. The algorithm verifies if the optimum is only local by carrying out a prefixed number of random mutation cycles. In the latter case, the best individual of the topographical domain is stored. The structure of the historical knowledge is analogous to the topographical one. The domain is initialized by a null solution. According to the aforementioned twofold aims, the influence function ih is also divided into two parts. 1) The first part is aimed at injecting randomness to avoid local optimum traps. If the same solution occurs for a given number of cycles, a new individual is generated by mutating some randomly selected chromosomes. If a better fitness arises, the corresponding individual substitutes the previous solution in the population space. 2) The second part is aimed at recovering a previous solution in order to reconsider a better past optimum by following the differential concept analogously, as for the topographical influence it . The update function uh adds any local optimum found during the evolutionary process to a suitable list with aims analogous to the topographical update. IV. E XPERIMENTAL R ESULTS The proposed approach was implemented under the framework of the Project PROMADAT, which was supported by the Italian Local Government of Regione Campania [24], for the company Quality Transport Consortium (CTQ), Ponticelli, Napoli, Italy, which is committed to tire-based transports of fuel for final distribution [25]. In the following, 1) the data acquisition section, 2) the implemented CA for fault isolation, 3) the CA validation, and 4) the CA performance testing are described.

A. Data Acquisition Section Each fleet vehicle is monitored by two master–slave units, namely 1) the Vehicle Monitoring Unit of Tractor UMAT and 2) the Vehicle Monitoring Unit of Tank UMAB (Fig. 6). Measured data, together with Global Positioning System data, are sent via the GPRS–GSM channel to a central station for synthetic monitoring and processing. The measured quantities are shown in Table I. The UMAT (Fig. 7) is mainly based on the following: • a microcontroller Rabbit RCM3400 for supervision and data acquisition; • an interface CAN bus CANgine (FMS standard [26]) for monitoring parameters of the DAF tractor engine; • 4–20 mA sensors for monitoring environment pollution (OPUS: carbon monoxide: 200 ppm, sulfur bioxide: 15 ppm, nitrogen monoxide: 100 ppm, nitrogen bioxide: 12 ppm) and weather (relative humidity (RH): 0–100%, bath pressure: 150–1150 mbar, temperature: −30 ◦ C to +70 ◦ C); • a keypad and a display for driver communications; • a wireless modem SIEMENS MC35i Terminal for GSM–GPRS data transmission; • a serial communication RS-422 to UMAB. The UMAB (Fig. 7) is mainly based on the following: • a microcontroller PIC 18 for supervision and data acquisition; • an interface RS 232 to the fuel volume measurement device SAMPI TE Dual 500; • sensors for monitoring safety operations (ATEX zone 1: capacitive M30 plastic 1G Eex ia IIB T6, 15 mm); • serial communication RS 422 to UMAT. B. Implemented CA The possible faults in the company activity were classified as follows: 1) danger; 2) fraud; 3) wear; and 4) management (Table II). In each class, the most serious faults were identified,


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TABLE I PERIPHERALS, FAULT CODES (FC), AND CSs

TABLE II FCs, FAULTS, AND a priori PROBABILITIES

Fig. 7. Two-controller master–slave device for vehicle measurement and data preprocessing: UMAT and UMAB.

for a total number of 19. The anomalies in signal output by the data acquisition section and the corresponding faults were associated by means of occurrence probabilities, which were obtained by both experimental data analysis and technicians’ experience. In particular, for each fault, the a priori probability of occurrence (Table II), the data acquisition peripherals showing an anomalous signal, and the corresponding conditioned probability of occurrence of the related anomalies in signals (i.e., causal strengths) were identified. This knowledge was input to the CA configuration module of the fault detection and isolation section (Fig. 4). As an example, the row of the matrix CS corresponding to an anomaly on the download duration (Table I), which is measured by the UMAB through the fuel meter, is [0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.5 0.0 0.7 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0], where the succession of elements corresponds to the fault list shown in Table II. An anomaly not arising from a

fault is coded as a causal strength of “0.0,” whereas an anomaly having a conditioned occurrence probability of 40% is coded as “0.4.” The nonnull causal strengths for each peripheral and for each fault are shown in Table I. This example, such as the following ones, was intentionally selected in order to be straightforward for the sake of the clarity of exposition. The CA was implemented in Matlab 7.0, and in particular, the population space exploited the Toolbox “GA Tool.” The population of the GA was composed of a given number of individuals (ranging from 20 to 100), which is constant during

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its evolution: An experiment showed that more individuals did not improve performance but did affect the computing time, whereas fewer individuals lead to premature convergence on local optimum. An individual includes 19 chromosomes, each one representing the conditioned probability of anomaly occurrence, given a fault. The initial population was randomly chosen. After analysis of the literature [27], [28] and several tests, the GA evolution operations were selected as follows: single-point crossover (80%), Gaussian mutation (20%), percentage of migration (20%), “elite count” (individual number unvaried from an evolution to the successive one: 3), and stall limit (maximum number of cycles with unvaried solution: 20). The GA evolution was stopped after a given number of cycles (30), and a given percentage (20%) of best individuals was identified in order to determine the characteristics to be inserted in the belief space. Parameters for the differential evolution were chosen such as F = 0.5 and CR = 1, according to [10]. Then, in the topographical knowledge domain, the best individuals with their cells (10%) as subsets of the solution space were inserted, and in the historical knowledge domain, best individuals of the topographical knowledge (5) with their cells were included. The number of rows in the topographical knowledge matrix was 10, the number of rows in the historical knowledge matrix was 5, and the number of CA evolution cycles was 10 (CN in Table III). The differential influence was carried out on the GA population (P op1). Each influence function (situational, topographical, and historical) produces a corresponding population (P op2, P op3, and P op4, respectively). The four populations are compared individual by individual in order to select the best population P op5 to be used in the next GA evolution. Finally, the GA restarts, and the previous steps are repeated until the same solution is found by the CA for a given number of times (10). For each evolution cycle of the CA, the GA will evolve several times; this implies that, in comparison to the GA, the CA is intrinsically slower. However, for complex problems of fault isolation (at least five anomalies and five faults, with related probabilities of occurrence), the CA will move toward a better solution faster than the GA itself, owing to the aforementioned mechanism of evolution pressure. C. CA Validation The implemented system was validated on field by experimental operations of the fault isolation during the company’s daily work. The CA was configured such as reported in the previous section. In the following, two experimental case studies are described, namely 1) tanker wheel slip and 2) fuel meter uncalibration. As far as the tanker wheel slip is concerned, an accident occurred to a tanker owing to excessive speed when it was raining, and the tires were also a little flat. The tanker data are monitored by sensors connected to UMAT. When a parameter results out of its nominal range, a warning is sent to the central base station via GPRS communication. As an example, in the case of the tanker wheel slip (Table I), the following signals point out an out-of-bound situation (Table I): rain, RH, atmospheric pressure, tire pressure, atmospheric temperature, round per

minute, speedometer, and brakes. These residuals identify the signal anomalies in the set A+ of the 4-tuple F a, A, C, A+ . According to these warnings, the central software automatically selects the corresponding rows of the matrix CS. The related submatrix is used by the CA for fault diagnosis. The first step is the random generation of a population composed of possible solutions of the problem, i.e., possible causes of the fault (individuals). Then, the fitness of each individual is computed by using (1)–(4) and the causal strength submatrix. The evolution of the CA takes place, and finally, the solution with the highest fitness, thus with the highest probability of a correct diagnosis, is selected as the best result. The possible faults corresponding to these anomalies and the related causal strengths are reported in Table II. The CA achieved the following solution: [0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0], where the value “1.0” of the third chromosome is related to the isolated fault: tanker wheel slip (D3 in Table II); this means that only this fault was isolated with a casual strength of 100%. The diagnosis quality, which is expressed as a fitness value, is equal to 5%. It has to be highlighted that this value arises from the product of three probability values according to (1), and thus, its amount has to be consequently assessed. As far as the fuel meter uncalibration is concerned, the following residuals were detected for the set A+ : total amount charged, loaded amount per tank, total amount distributed, download duration, and amount downloaded per tank. The possible faults corresponding to these anomalies and the related causal strengths are reported in Table II. In this case, the CA achieved the following solution: [0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 0.0 0.0 0.0 0.0], where the nonnull value of 0.9 corresponds to the isolated fault of the “fuel meter uncalibration” (Table II). The diagnosis quality, which is expressed as a fitness value, is equal to 0.9%. In addition, this value has to be assessed by considering it to be the product of three probability values. D. CA Performance Testing According to the literature about evolutionary algorithms [11], the main performance indexes are effectiveness (a measure of the quality solution within a given computational limit) and efficiency (a measure of the amount of computing needed to achieve a satisfactory solution). CA effectiveness and efficiency were compared with a reference GA. With this aim, the CA and the reference GA were equivalently configured. In particular, the GA was designed as the CA population space, in order to point out specifically the performance variation due to the cultural drive of evolution. Two kinds of tests were carried out by referring both 1) to a literature case study [1] and 2) to the aforementioned on-field case study of dangerous transport. 1) Literature Case Study: This study aimed to show the performance improvement in a literature GA, owing to the addition of a belief space structure. As far as the effectiveness is concerned, the reference GA was configured according to Miller et al. [1] because the fitness was the same as (1). The Miller case study consists of 10 signal anomalies and 15 faults. The Miller GA is based on binary chromosomes for a space


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TABLE III RESULTS OF PERFORMANCE COMPARISON BETWEEN CA AND GA (CN: CYCLE NUMBER; CS: CAUSAL STRENGTH; FIT: FITNESS)

of 215 solutions, whereas the CA explores a space of ∞15 solutions, which is based on real chromosomes (i.e., the CA also makes available a probability of fault occurrence). The GA worked according to the following configuration: crossover probability: 0.6; cycle number: 50; and population size: 140. The CA was forced to evolve for ten cycles, each one including five population space cycles, for a total of 50 cycles analogously as the reference GA. On average (30 tests), the best individual isolated by the reference GA was [0 0 1 1 0 0 0 0 1 0 0 1 1 0 0] with a fitness of 7.7%. The best individual isolated by the CA was [0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.7 1.0 0.0 0.0 1.0 0.0 0.0 0.0] with a fitness of 58.0%. The CA solution is remarkably better, because the CA and GA are based on the same fitness function. As far as the efficiency test is concerned, the target effectiveness was defined to be equal to the fitness value of 7.7% that was reached by the reference GA in the aforementioned effectiveness test. The CA and the GA were configured such as in the aforementioned effectiveness test. On average (30 tests), the CA after two cycles (equivalent to 2 × 5 = 10 cycles of the reference GA) reached the fitness target, whereas the GA performed 50 cycles as discussed previously. 2) On-Field Case Study of Dangerous Transport: As far as the effectiveness test is concerned, the CA was configured as follows: number of cycles: 10; number of cycles of the population space: 15; and number of population individuals: 100. The reference GA was designed with an equivalent number of cycles equal to 150 (i.e., 10 × 15), as well as with 100 individuals. On this basis, the aforementioned case studies, namely 1) tanker wheel slip and 2) fuel meter uncalibration, were analyzed both by the CA and the reference GA. In the tanker wheel slip analysis, an a priori probability of 20%, which was suggested by skilled technicians, was used for the CA. The obtained test results are reported in Table III. On average (30 tests), the CA solution assigned a causal strength of 100.0% to the fault “tanker wheel slip” and 0.0% to all other faults. The corresponding value obtained for the fitness (fit) was 5.0%. Conversely, for the same total number of evolutions as mentioned previously, on average, the GA solution assigned a causal strength of 95% to the fault “tanker wheel slip,” but without isolating it totally, and by randomly pointing some other faults, with an occurrence probability of about 10%. The corresponding value for the final fitness was 0.2% (Table III). In the fuel meter uncalibration analysis, an a priori probability of 45% was suggested for the CA. On average (30 tests), the CA solution assigned a causal strength of 90% to the fault “fuel meter uncalibration” and 0% to all other faults. The corresponding value for the final fitness fit is about 0.9%. Conversely, for the same total number of evolutions, as mentioned before,

on average, the GA solution assigned a causal strength of 80% to the fault “fuel meter uncalibration,” but without isolating it totally, and by pointing some other faults randomly, with an occurrence probability of about 10%. The corresponding value for the final fitness is about 0.1%. As far as the efficiency test is concerned, the CA and the GA were configured as in the aforementioned effectiveness test by measuring the cycle number necessary to reach the fitness values previously obtained by the CA in the effectiveness test, again for the aforementioned case studies, namely 1) tanker wheel slip and 2) fuel meter uncalibration. In the tanker wheel slip analysis, on average, the CA reached the above target value of 5.0% after nine cycles. Conversely, the GA was not capable of reaching the above target and, after 13 500 cycles, was stopped, with an obtained fitness value of 3.9%. In the fuel meter uncalibration analysis, on average, the CA reached the above target value of 0.9% after seven cycles (Table III). Conversely, the GA was capable of reaching the above target after 6753 cycles.

V. C ONCLUSION An automatic fault isolation approach, which is based on a CA and a remote real-time monitoring system, has been proposed and on-field tested for the transport of dangerous goods. Experiments were carried out on three main innovations, namely 1) a cultural mechanism of evolution driving to avoid intrinsic resource waste of genetic applications, which is capable of extracting better characteristics of evolving solutions and using them for a successive optimum track; 2) a differential concept in evolution influence in order to sense the fitness gradient in the solution space toward the optimum; and 3) a general-purpose design of the software architecture to be application-independent and easily customizable by technical final users. Experimental results of the performance comparison with Matlab standard GA showed the usefulness in terms of effectiveness and efficiency of the cultural mechanism as well as of the differential influence on the solution search. Future work will be devoted to a more comprehensive specific investigation on the fault aliasing problem, which may be an important factor affecting the performance of fault isolation.

ACKNOWLEDGMENT The authors would like to thank Alberto “Holy Jo” Ferro for gathering the experimental data.

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R EFERENCES [1] J. A. Miller, W. D. Potter, R. V. Gandham, and C. N. Lapena, “An evaluation of local improvement operators for genetic algorithms,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 5, pp. 1340–1351, Sep./Oct. 1993. [2] X. Jin and R. G. Reynolds, “Data mining using cultural algorithms and regional schemata,” in Proc. 14th Int. Conf. Tools Artif. Intell., Washington, DC, Nov. 2–5, 2002, pp. 33–40. [3] G. B. Fogel and D. B. Fogel, “Continuous evolutionary programming: Analysis and experiments,” Cybern. Syst., vol. 26, no. 1, pp. 79–90, 1995. [4] W. Gao, “Comparison study of genetic algorithm and evolutionary programming,” in Proc. 3rd Int. Conf. Mach. Learn. Cybern., Shanghai, China, Aug. 26–29, 2004, pp. 204–209. [5] R. G. Reynolds and W. Sverdlik, “Problem solving using cultural algorithms,” in Proc. 1st IEEE Conf. Evol. Comput., Orlando, FL, Jun. 27–29, 1994, pp. 645–650. [6] J. G. Digalakis and K. G. Margaritis, “A multipopulation cultural algorithm for the electrical generator scheduling problem,” Math. Comput. Simul., vol. 60, no. 3, pp. 293–301, Sep. 2002. [7] M. Stenberg and R. G. Reynolds, “Using cultural algorithms to support re-engineering of rule-based expert systems in dynamic performance environments: A case study in fraud detection,” IEEE Trans. Evol. Comput., vol. 1, no. 4, pp. 225–243, Nov. 1997. [8] N. Rychtyckyj and R. G. Reynolds, “Knowledge base maintenance using cultural algorithms: Application to the DLMS manufacturing process planning system at Ford Motor Company,” in Proc. Congr. Evol. Comput., Honolulu, HI, May 12–17, 2002, pp. 855–860. [9] S. Saleem and R. G. Reynolds, “The impact of environmental dynamics on cultural emergence,” in Festschrift, in Honor of John Holland. New York: Oxford Univ. Press, 2003, pp. 1–10. [10] R. L. Becerra and C. A. C. Coello, “Culturizing differential evolution for constrained optimization,” in Proc. 5th Mexican Int. Conf. Comput. Sci., Colima, Mexico, 2004, pp. 304–311. [11] A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing. Berlin, Germany: Springer-Verlag, 2003, ch. 14. [12] K. V. Price, “An introduction to differential evolution,” in New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover, Eds. London, U.K.: McGraw-Hill, 1999, pp. 79–108. [13] R. Storn, “System design by constraint adaptation and differential evolution,” IEEE Trans. Evol. Comput., vol. 3, no. 1, pp. 22–34, Apr. 1999. [14] T. Rogalsky, R. W. Derksen, and S. Kocabiyik, “Differential evolution in aerodynamic optimization,” in Proc. 46th Annu. Conf. Can. Aeronautics Space Inst., Montréal, PQ, Canada, May 2–5, 1999, pp. 29–36. [15] N. K. Madavan, “Multiobjective optimization using a Pareto differential evolution approach,” in Proc. CEC, Honolulu, HI, May 12–17, 2002, vol. 2, pp. 1145–1150. [16] H.-Y. Fan and J. Lampinen, “A trigonometric mutation operation to differential evolution,” J. Glob. Optim., vol. 27, no. 1, pp. 105–129, Sep. 2003. [17] G. Betta, C. Liguori, and A. Pietrosanto, “The use of genetic algorithm for advanced instrument fault detection and isolation schemes,” in Proc. Instrum. Meas. Technol. Conf., Brussels, Belgium, Jun. 4–6, 1996, pp. 1129–1134. [18] F. Russo, “Noise removal from image data using recursive neurofuzzy filters,” IEEE Trans. Instrum. Meas., vol. 49, no. 2, pp. 307–314, Apr. 2000. [19] M. Pastorino, S. Caorsi, and A. Massa, “A global optimization technique for microwave nondestructive evaluation,” IEEE Trans. Instrum. Meas., vol. 51, no. 4, pp. 666–673, Aug. 2002. [20] D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989. [21] J. R. Koza, F. H. Bennett, III, D. Andre, and M. A. Keane, Genetic Programming III: Darwinian Invention and Problem Solving. San Mateo, CA: Morgan Kaufmann, 1999. [22] Y. Peng and J. A. Reggia, “A probabilistic causal model for diagnostic problem solving, Part I: Integrating symbolic causal inference with numeric probabilistic inference,” IEEE Trans. Syst., Man, Cybern., vol. SMC-17, no. 2, pp. 146–162, Mar./Apr. 1987. [23] J. L. Bentley and J. H. Friedman, “Data structures for range searching,” ACM Comput. Surv., vol. 11, no. 4, pp. 397–409, Dec. 1979. [24] Regione Campania, PROMADAT: A real-time remote system for monitoring and diagnostics of vehicles for dangerous good transport, Nov. 26, 2003. (POR Measure 3.17) (in Italian). [25] Eur. Patent Application NA2004A000039. [26] [Online]. Available: http://www.fms-standard.com

[27] S. Caorsi, A. Massa, M. Pastorino, A. Randazzo, and A. Rosani, “A reconstruction procedure for microwave nondestructive evaluation based on a numerically computed Green’s function,” IEEE Trans. Instrum. Meas., vol. 53, no. 4, pp. 987–992, Aug. 2004. [28] F. Russo, “Evolutionary neural fuzzy systems for noise cancellation in image data,” IEEE Trans. Instrum. Meas., vol. 48, no. 5, pp. 915–920, Oct. 1999.

Pasquale Arpaia was born in Napoli, Italy, on February 2, 1961. He received the M.S. and Ph.D. degrees in electrical engineering from the University of Napoli Federico II. Until 2001, he taught electrical and electronic measurements with the University of Napoli Federico II, where he was a member of the Scientific and Administration Council of the new thematic University of Science and Technology. Then, he joined the Department of Engineering, University of Sannio, Benevento, Italy, where he is currently an Associate Professor. Since August 2005, he has been Project Associate for the Large Hadron Collider with the European Organization for Nuclear Research (CERN). Since May 2007, he has been a Scientific Associate with the Engine Institute of the Italian National Council of Research. He has been a Consultant on the European Union (EU) IV Framework Programme “Standard Measurement and Testing” and an Evaluator for EU INTAS projects. He is responsible, with H. Schumny, for the Promoting Committee of the EUPAS Project of the IMEKO TC-4 “A/D and D/A Metrology” WG. He is an Associate Editor for “Digital Instruments Standardization” for the Elesevier Journal Computer Standards and Interfaces. His main research interests include ADC modeling, testing, and standardization, measurement systems on geographic networks, statistical-based characterization of measurement systems, evolutionary diagnostics, and digital instruments for magnetic measurements in particle accelerators. In these fields, he has published more than 130 scientific papers in journals and national and international conference proceedings. Dr. Arpaia is a Voting Member of the IEEE IM TC-10 “Waveform Measurement and Analysis” and the IEC TC-47. He is an Associate Editor for the subject areas “Quality and Statistical Methods” and “Test” of the IEEE TRANSACTIONS ON ELECTRONICS PACKAGING AND MANUFACTURING. He has organized several international meetings in the field of electronic measurements and European cooperation.

Giuseppe Lucariello was born in Naples, Italy, in November 1978. He received the M.S. degree in mechanical engineering from the Facoltà d’Ingegneria di Napoli “Federico II,” Naples, in 2003. He is currently with the Department of Engineering, University of Sannio, Benevento, Italy, where he has been a Research Fellow for industrial research projects in the automotive field. His main interests are fault diagnosis and isolation and measurements for qualification.

Antonio Zanesco was born in Naples, Italy, in February 1975. He received the M.S. degree in electronic engineering from the Facoltà d’Ingegneria di Napoli “Federico II,” Naples, in 2001. He was with the Centre Nationale Etudes Spatiales, Toulouse, France, until 2003, where he worked for 18 months in the field of optoelectronic measurements. He was a Researcher for industrial research projects in the automotive field and in the biomedical measurement field. He developed software for remote monitoring and remote diagnosis with artificial intelligence. He is currently a Research Fellow with the Department of Engineering, University of Sannio, Benevento, Italy. His main interests are artificial intelligence, fault diagnosis and isolation, and EIS measurements.