Combining Multiple Diagnostic Trouble Codes into a Single Decision Tree

Combining Multiple Diagnostic Trouble Codes into a Single Decision Tree

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Preprints, 8th IFAC International Symposium on Preprints, IFAC Advances in Automotive Control Symposium Preprints, 8th 8th IFAC International International Symposium on on Preprints, 8th IFAC International Symposium on Advances in Automotive Control June 19-23, 2016. Norrköping, Sweden Available online at www.sciencedirect.com Advances in Automotive Control Advances in2016. Automotive Control June 19-23, Norrköping, Sweden June June 19-23, 19-23, 2016. 2016. Norrköping, Norrköping, Sweden Sweden

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IFAC-PapersOnLine 49-11 (2016) 555–561

Combining Multiple Diagnostic Combining Multiple Diagnostic Combining Multiple Diagnostic Codes into a Single Decision Codes into a Single Codes into a Single Decision Decision

Trouble Trouble Trouble Tree Tree Tree

Markos F. B. G. Oliveira ∗∗ Ricardo L¨ uders ∗∗ ∗ Ricardo L¨ Markos F. B. G. Oliveira u ders ∗∗ ∗ Markos F. B. G. Oliveira Ricardo L¨ u Markos F. B. G. Oliveira Ricardo L¨ uders ders ∗ Federal University of Technology Paran´ a , Curitiba, 80230-901 PR ∗ ∗ University of Technology -- Paran´ a ,, Curitiba, 80230-901 PR ∗ Federal Federal University of Technology Paran´ a Curitiba, Brazil (e-mail: [email protected], [email protected]). Federal University of Technology - Paran´ [email protected]). , Curitiba, 80230-901 80230-901 PR PR Brazil (e-mail: [email protected], Brazil (e-mail: [email protected], [email protected]). Brazil (e-mail: [email protected], [email protected]). Abstract: Recent advances in design of automotive systems have yielded to vehicles that now Abstract: Recent advances in design of automotive systems have yielded to vehicles that now Abstract: Recent advances in of systems have yielded to vehicles now include several complex electronic devices. They are able to log hundreds trouble Abstract: Recent advances in design design of automotive automotive systems have yielded of to diagnostic vehicles that that now include several complex electronic devices. They are able to log hundreds of diagnostic trouble include(DTC) severalfor complex electronic devices. conditions They are are able able toa log log hundreds of of fault diagnostic trouble codes different malfunctioning withto corresponding tree built for include several complex electronic devices. They hundreds diagnostic trouble codes (DTC) for different malfunctioning conditions with a corresponding fault tree built for codes (DTC) for different malfunctioning conditions with aa corresponding fault tree built for each code. However, many of these active codes can have common causes which make diagnostic codes (DTC) for different malfunctioning conditions with corresponding fault tree built for each code. However, many of these active codes can have common causes which make diagnostic code. However, many of these active codes can have common causes which make diagnostic aeach very complex task. Using binary decision diagrams (BDD), we propose a strategy for combining each code. However, many of these active codes can have common causes which make diagnostic aa very task. Using decision diagrams (BDD), we strategy combining very complex complex task. Using binary binary decision diagrams (BDD), we propose propose strategy for combining individual BDDs developed for each DTC into a single one. This strategyaaa takes intofor account sets aindividual very complex task. Using binary decision diagrams (BDD), we propose strategy for combining BDDs developed for each DTC into a single one. This strategy takes into account sets individual BDDs developed for DTCs each DTC DTC into aa single single one. Thisdiagnostic strategy takes takes into account account sets of both active and non-active representing the one. current scenario. The results individual BDDs developed for each into This strategy into sets of both active and non-active DTCs representing the current diagnostic scenario. The results of both active and non-active DTCs representing the current diagnostic scenario. The results show a significant decrease in the total diagnostic cost. of both active and non-active DTCs representing the current diagnostic scenario. The results show aa significant decrease in the total diagnostic cost. show decrease in diagnostic cost. show a significant significant decreaseFederation in the the total total diagnostic cost. Hosting by Elsevier Ltd. All rights reserved. © 2016, IFAC (International of Automatic Control) Keywords: Fault diagnosis, fault tree analysis, binary decision diagram, automotive system. Keywords: Fault Fault diagnosis, fault fault tree analysis, analysis, binary decision Keywords: decision diagram, diagram, automotive automotive system. system. Keywords: Fault diagnosis, diagnosis, fault tree tree analysis, binary binary decision diagram, automotive system. 1. INTRODUCTION cause of the system fault (Dutuit and Rauzy, 2013). Thus 1. INTRODUCTION INTRODUCTION cause of (Dutuit and Thus 1. cause of the theasystem system fault (Dutuit and Rauzy, Rauzy, 2013). Thus it provides naturalfault measure to define which2013). component 1. INTRODUCTION cause of the system fault (Dutuit and Rauzy, 2013). Thus it provides a natural measure to define which component it provides a natural measure to define which component should be tested first. In the last years there was a significant improvement it provides a natural measure to define which component be tested first. In diagnostic the last last years there was was aa significant significant improvement should be first. In the there improvement in of automotive by providing vehi- should should be tested tested first. In the last years years there was systems a significant improvement In this work, we propose a diagnostic strategy that builds in diagnostic of automotive systems by providing vehiin diagnostic of automotive systems by providing vehicles with complex electronic devices (Suwatthikul, 2010). In this work, we propose aa diagnostic strategy builds in diagnostic of automotive systems by providing vehiIn this work, we propose strategy that builds a single BDD by combining multiple BDDs of that active and cles with complex electronic devices (Suwatthikul, 2010). In this work, we propose a diagnostic diagnostic strategy that builds cles with complex electronic (Suwatthikul, 2010). Embedded electronic systemsdevices are now able to store lots a single BDD by combining multiple BDDs of active and cles with complex electronic devices (Suwatthikul, 2010). a single BDD by combining multiple BDDs of active non-active DTCs. Therefore, current BDDs already develEmbedded electronic systems are now now able able to store store lots a single BDD by combining multiple BDDs of active and and Embedded electronic systems are to lots of diagnostic information as Diagnostic Trouble Codes non-active DTCs. Therefore, current BDDs already develEmbedded electronic systems are now able to store lots non-active DTCs. current BDDs already developed for each DTCTherefore, can be used, preserving diagnostic efof diagnostic diagnostic information ascomponents. Diagnostic These Trouble Codes non-active DTCs. Therefore, current BDDs already develof information as Diagnostic Trouble Codes (DTCs) generated by faulty codes are oped for DTC used, efof diagnostic information ascomponents. Diagnostic These Trouble Codes oped made for each each DTC can can be used, preserving preserving diagnostic efforts previously bybe industry. In the end,diagnostic unnecessary (DTCs) generated by faulty codes are oped for each DTC can be used, preserving diagnostic ef(DTCs) generated by faulty These codes are triggered when a system failcomponents. occurs. During maintenance forts made previously by industry. In the end, unnecessary (DTCs) generated by faulty components. These codes are forts made previously by industry. In the end, unnecessary tests are avoided based on the current diagnostic scenario. triggered when a system fail occurs. During maintenance forts made previously by industry. In the end, unnecessary triggered system fail During maintenance or repair, when DTCsaa can be recovered from internal vehicle’s tests are avoided based on the current diagnostic scenario. triggered when system fail occurs. occurs. During maintenance tests are based the diagnostic similar idea can beon in Yuan and Huscenario. (2009), or repair, repair,by DTCs can betechnicians recovered from internal vehicle’s A tests are avoided avoided based onfound the current current diagnostic scenario. or DTCs can be recovered from internal vehicle’s memory specialized using guided diagnostic can in (2009), or repair, DTCs can be recovered from internal vehicle’s A A similar similar idea can be be found found in Yuan Yuanof and and Hu (2009), but it doesidea not consider any measure test Hu importance memory by specialized technicians using guided diagnostic A similar idea can be found in Yuan and Hu (2009), memory by specialized technicians using guided tools. However, many DTCs are triggered due todiagnostic a cascade but it does not consider any measure of test importance memory by specialized technicians using guided diagnostic butdiagnostic it does does not notcost. consider any related measurework of test test importance or Another canimportance be found tools. However, many DTCs are triggered due to a cascade but it consider any measure of tools. However, DTCs are due to effect caused bymany common faulty components diag- or diagnostic cost. Another related work can be found tools. However, DTCs are triggered triggered duemaking to aa cascade cascade or diagnostic cost. related work be and Dugan (2008) where evidences provided by effect caused caused bymany common faulty components making diag- in or Assaf diagnostic cost. Another Another related work can can be found found effect by common components making diagnostic a burden and timefaulty consuming task. For example, Assaf and Dugan (2008) where evidences provided by effect caused by common faulty components making diag- in in Assaf and Dugan (2008) where evidences provided monitors and sensors are incorporated into diagnostic. A nostic a burden and time consuming task. For example, Assaf and Dugan (2008) where evidences provided by by nostic aa burden time consuming task. For example, an electrical faultand caused by a short-circuit to ground in in monitors and sensors are incorporated into diagnostic. A nostic burden and time consuming task. For example, monitors and and sensors are by incorporated into diagnostic. A diagnostic costsensors proposed the authors is used to show an electrical fault caused by a short-circuit to ground in monitors are incorporated into diagnostic. A an electrical fault caused by aa short-circuit ground aancable connecting an electronic control unitto (ECU) to in a diagnostic cost proposed by the authors is used to show electrical fault caused by short-circuit to ground in diagnostic cost proposed by the authors is used to show that a less cost complex diagnostic is then obtained. However, a cable connecting an electronic control unit (ECU) to a diagnostic proposed by the authors is used to show atemperature cable control unit to sensor an canelectronic trigger the DTCs for(ECU) both ECU aadoless is then obtained. However, atemperature cable connecting connecting control unit to aa that that complex diagnostic is obtained. However, they notcomplex considerdiagnostic multiple FTs. The advantage of our sensor an canelectronic trigger the the DTCs for(ECU) both ECU ECU that adoless less complex diagnostic is then then obtained. However, temperature can trigger DTCs for both and sensor. sensor they not consider multiple FTs. The advantage of our temperature sensor can trigger the DTCs for both ECU they do not consider multiple FTs. The advantage of strategy is to reduce the diagnostic cost while dealing and sensor. they do not consider multiple FTs. Thecost advantage of our our and sensor. strategy is to reduce the diagnostic while dealing and sensor. strategy is to tofaults. reduceThis theis diagnostic diagnostic cost an while dealing with multiple shown by using illustrative The diagnostic of such systems are based on fault trees strategy is reduce the cost while dealing multiple faults. shown byreal-world using an illustrative The diagnostic diagnostic of such such systems arespecialists. based on on fault fault trees trees with with multiple faults. This is shown using example, although it This is notis study case The of are based analysis (FTA) built by systems diagnostic with multiple faults. This isreplace shown aaby byreal-world using an an illustrative illustrative The diagnostic of such systems arespecialists. based on Fault fault trees example, although it is not replace study case analysis (FTA) built by diagnostic Fault trees example, although it is not replace a real-world study to be considered in the future. analysis (FTA) built by diagnostic specialists. Fault trees are often partitioned into vehicles’ subsystems and mainexample, although it is not replace a real-world study case case analysis (FTA) built by diagnostic specialists. Fault trees to be considered in the future. are often partitioned into vehicles’ subsystems and mainbe considered in the future. are often into vehicles’ maintained by partitioned even different teams. Onesubsystems fault tree isand built for to to be considered in the future. are often partitioned into vehicles’ subsystems and mainThis paper is organized as follows. Section 2 presents a tainedDTC by even even different teams. teams. One fault tree is is builttofor for tained by different fault tree each by associating causes One (faulty components) it This paper is organized as follows. Section 2 presents a tained by even different teams. One fault tree is built builttofor This follows. Section 22 presents aa background onorganized fault tree as analysis. 3 describes each DTC by associating causes (faulty components) it This paper paper is is organized as follows.Section Section presentsthe each DTC by causes components) to it and then into a diagnostic decision tree (DTC) on fault tree analysis. Section 33 describes the each DTCencoded by associating associating causes (faulty (faulty components) to to it background background on fault tree analysis. Section describes the problem of dealing with multiple fault codes. Section and then encoded into a diagnostic decision tree (DTC) to background on fault with tree analysis. Section 3 describes the4 and then into aa component diagnostic implement sequences tests. tree of fault codes. Section and then encoded encoded intoof diagnostic decision decision tree (DTC) (DTC) to to problem problem our of dealing dealing with multiple fault codes.BDDs Section presents strategy for multiple combining multiple and444 implement sequences of component tests. problem of dealing with multiple fault codes. Section implement sequences of component tests. strategy combining multiple and implement sequences presents our strategy for combining multiple BDDs and Section 5our presents an for illustrative example by BDDs comparing The conversion processofofcomponent a fault treetests. to the corresponding presents presents our strategy for combining multiple BDDs and Section 5 presents an illustrative example by comparing The conversion process of a fault tree to the corresponding Section 5 presents an illustrative example by comparing diagnostic costs obtained for different approaches. ConThe conversion process of a fault tree to the corresponding DTC is discussed in Rauzy (1993); Reay and Andrews Section 5 presents an illustrative example by comparing The conversion process of a fault tree to the corresponding costs obtained for different ConDTC is is Remenyte discussed in in Rauzy (1993); Reayusing and aAndrews Andrews diagnostic costs obtained for approaches. Concluding remarks presented in Sectionapproaches. 6. DTC discussed (1993); Reay and (2002); andRauzy Andrews (2006) binary diagnostic diagnostic costs are obtained for different different approaches. ConDTC is Remenyte discussed in Rauzy (1993); Reayusing and aAndrews cluding remarks are presented in Section 6. (2002); and Andrews (2006) binary cluding remarks remarks are are presented presented in in Section Section 6. 6. (2002); Remenyte and Andrews (2006) using aa particbinary decision diagram (BDD). This process requires cluding (2002); Remenyte and Andrews (2006) using binary decision diagram (BDD). ThisInprocess process requires partic2. PRELIMINARIES decision diagram (BDD). This requires particular ordering of basic events. our work, this aa ordering decision diagram (BDD). ThisInprocess requires partic2. PRELIMINARIES ular ordering ofthe basic events. our work, work, this aordering ordering 2. ular ordering of basic events. In our this is provided by cost of diagnostic importance factor 2. PRELIMINARIES PRELIMINARIES ular orderingbyofthe basic events. In our work, this ordering is provided cost of diagnostic importance factor is provided by the cost of diagnostic importance factor (CDIF) introduced by Assaf and Dugan (2008). Among Most material presented in this section concerns building is provided by the cost of diagnostic importance factor (CDIF) indicators introducedfound by Assaf Assaf and literature Dugan (2008). (2008). Among Most material presented this concerns (CDIF) introduced by and Dugan Among material presented inminimal this section section concerns building several in the for assessing aMost BDD that encodes thein solutions of a building boolean (CDIF) introducedfound by Assaf and literature Dugan (2008). Among Most material presented in this section concerns building several indicators in the the for assessing aafunction BDD that encodes the minimal solutions of aa DIF boolean several indicators found in literature for assessing BDD that encodes the minimal solutions of boolean risk significance, the diagnostic importance factor (DIF) as presented in Rauzy (1993). Moreover, and several indicators found in the literature for assessing a BDD that encodes the minimal solutions of a boolean risk significance, significance, the diagnostic diagnostic importance factor (DIF) function as presented in Rauzy (1993). Moreover, DIF and risk the importance factor (DIF) function as presented in Rauzy (1993). Moreover, DIF and measures the probability of a particular basic event be the CDIF are defined according to Dutuit and Rauzy (2013) risk significance, the diagnostic importance factor (DIF) function as presented in Rauzy (1993). Moreover, DIF and measures the probability of a particular basic event be the CDIF are defined according to Dutuit and Rauzy (2013) measures the probability of a particular basic event be the CDIF are defined according to Dutuit and Rauzy (2013) and Assaf and Dugan (2008), respectively. ⋆ measures probability of aundergraduate particular basic event be the CDIF are defined according to Dutuit and Rauzy (2013) This workthe is supported by the research program of and Dugan (2008), respectively. ⋆ and Assaf Assaf1.and and Dugan (2008), respectively. This work is supported by the undergraduate program of ⋆ and Assaf and (2008), Example A Dugan fault tree is a respectively. graphical representation of PROPPG/UTFPR. A long support is also research recognized from the This by the research program of ⋆ This work work is is supported supported byterm the undergraduate undergraduate research program of Example 1. A fault tree is a graphical of PROPPG/UTFPR. A long term support is also recognized from the Example 1. A fault tree is a representation of (logical) combinations of basic events representation (or components) Brazilian agencies CNPq, CAPES and Funda¸ c ˜ a o Arauc´ a ria. PROPPG/UTFPR. A long term support is also recognized from the Example 1. A fault treeofisbasic a graphical graphical representation of PROPPG/UTFPR. A longCAPES term support is also recognized from the (logical) combinations events (or components) Brazilian agencies CNPq, and Funda¸ c ˜ a o Arauc´ a ria. (logical) combinations of basic events (or components) Brazilian agencies CNPq, CAPES and Funda¸ c ˜ a o Arauc´ a ria. (logical) combinations of basic events (or components) Brazilian agencies CNPq, CAPES and Funda¸c˜ ao Arauc´ aria. Copyright © 2016, 2016 IFAC 565 Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International Federation of Automatic Control) Copyright © 2016 IFAC 565 Copyright © 2016 IFAC 565 Peer review under responsibility of International Federation of Automatic Copyright © 2016 IFAC 565Control. 10.1016/j.ifacol.2016.08.081

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Definition 6. (Coherent boolean function). A function s is coherent if for any two minterms σ and π such that σ ≼ π, σ ∈ s implies that π ∈ s. In other words, faulty components that cause a system fail must cause fail for all other component conditions.

Fig. 1. Fault tree for Example 1. that can be faulty which generates a system fail s as depicted in Fig. 1. Suppose a system with components c1 , . . . , c6 that can be faulty which generates an observed system fail. A component is set to 1 when it is faulty and 0 otherwise. Similarly s is set to 1 (active) under system fail and 0 otherwise. The relationship between system fail and faulty components is given by the boolean expression (1). s = c 6 c 4 + c5 c 2 + c2 c 3 c 1 (1) A strategy of diagnostic is then a sequence of tests (pass or failed) to identify faulty components causing a system fail.

Given an expression s, s|c1 , . . . , ck is the boolean expression evaluated for c1 = · · · = ck = 1 and c1 = · · · = ck = 0 in s. Proposition 7. (Logical Shannon decomposition). Let s be a boolean expression and c ∈ var(s). Then, s ≡ c · (s|c) + c · (s|c) (2) Proposition 8. (Probabilistic Shannon decomposition). Let s be a boolean expression and c ∈ var(s). Then, Pr{s} = Pr{c} · Pr{s|c} + (1 − Pr{c}) · Pr{s|c} (3) = Pr{c} · (Pr{s|c} − Pr{s|c}) + Pr{s|c} Let π be a positive product of boolean variables of C. The minterm obtained by completing π with negative literals of C that do not appear in π is denoted by ⌊π⌋C . Definition 9. (Minimal cutset). Let s be a boolean function and π be a product of boolean variables of var(s). Then π is a cutset of s if ⌊π⌋var(s) ∈ s. In addition, π is a minimal cutset of s if it is a cutset of s and there is no cutset σ of s such that σ ≺ π. Proposition 10. Let s be a boolean function and MCS(s) be the disjunction of minimal cutsets of s. Then s is coherent iff MCS(s) ≡ s.

2.1 Minimal cutsets

2.2 DIF and CDIF

Boolean variables are denoted by letters c, c1 , c2 , . . . , while s, s1 , s2 , . . . denote boolean expressions. Variables occurring in a boolean expression s are given by var(s). Definition 2. (Boolean expression). A boolean expression over an enumerable set C of variables (components or basic events) is recursively defined by (i) a variable is a boolean expression; (ii) if s1 and s2 are boolean expressions, then s1 + s2 , s1 · s2 and s1 are boolean expressions as well. Definition 3. (Variable assignment). An assignment σ of var(s) ∈ C is a function from var(s) into {0, 1}. Definition 4. (Boolean function). A boolean function of n variables is a mapping f : {0, 1}n → {0, 1}. Definition 5. (Minterm). A minterm is a product of variables c or c (not both) for all c ∈ var(s).

DIF measures the importance of a component for diagnostic, i.e. it is the probability of a particular component is faulty given the system is faulty. Definition 11. (DIF). Given a boolean function s and a component c ∈ C, the diagnostic importance factor DIF(s, c) is given by Pr{c · s} . DIF(s, c) = Pr{c|s} = (4) Pr{s}

There is a one-to-one mapping between variable assignments and minterms. Given an assignment σ and a minterm π, c ∈ var(π) if σ(c) = 1 and c ∈ var(π) if σ(c) = 0. Therefore, σ or π are used interchangeable to denote variable assignments or minterms. Similarly, there is a unique boolean function corresponding to a given boolean expression (not the opposite). Therefore, we refer interchangeable to s as a boolean expression or function according to convenience. A partial order is defined in the set of minterms built over C (and consequently in the set of variable assignments of C). Given two minterms σ and π and c ∈ C, σ ≼ π iff c ∈ σ then c ∈ π. 566

It measures the risk of system fail due to a particular component fault c. It can be approximated by (5) and (6) according to Dutuit and Rauzy (2013).

with Pr{s|c} ≈

Pr{c} · Pr{s|c} DIF(s, c) ≈ ∑ π∈MCS(s) Pr{π} ∑

c·π∈MCS(s)

Pr{π} +



(5)

Pr{π}

(6)

π∈MCS(s),c∈π /

Assaf and Dugan (2008) have introduced CDIF. It includes a cost for component test as, for instance, time required for testing or test complexity. Definition 12. (CDIF). Given a boolean function s and a component c ∈ C whose cost of testing is tc , the cost of importance factor CDIF(s, c) is given by DIF(s, c) CDIF(s, c) = (7) tc

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is the unreliability of component c ∈ C. Given a system s with n cutsets, the system unreliability Qs is given by n ∏ Qs = 1 − (1 − qcutseti ) (9) i=1

where qcutseti is a product of unreliability values of c in the cutset i. 2.5 Diagnostic Cost (DC) Assaf and Dugan (2008) have introduced a formula to evaluate the cost of a particular BDD. Given a system s with n cutsets the diagnostic cost DC(s) is given by n 1 ∑ · qcutseti · cpi (10) DC(s) = Qs i=1

Fig. 2. BDD for Example 1.

j=1

CDIF is a tradeoff between test importance and cost. It is used here to provide the ordering of components to be tested. A component with high CDIF should be tested first either because it is important for diagnostic (high DIF) or because it is an inexpensive test (low cost). 2.3 Binary Decision Diagram (BDD) We assume the reader is familiar with most details of BDDs. A comprehensive introduction and algorithms are presented in Rauzy (1993). Definition 13. (BDD). A BDD is a directed acyclic graph with leaves ONE and ZERO encoding the constant boolean functions 1 and 0, respectively. Internal nodes are labeled by components c ∈ C with 1- and 0-outedges. It encodes a boolean function s = c · s1 + c · s0 where s1 and s0 are boolean functions encoded by nodes pointed by 1and 0-outedges of c, respectively. A BDD is built from a boolean expression by encoding its minimal cutsets. The corresponding BDD of Example 1 is given by Fig. 2. It encodes (1) by sharing common subtrees. Each path starting from the root to the leaves ONE or ZERO corresponds to a diagnostic sequence of tests. For example, a system fail caused by faulty components c6 , c5 and c2 is diagnosed by testing c6 , c4 , c5 , and c2 in Fig. 2. 2.4 Failure rate and unreliability The failure rate λ(t) of a component is the expected number of failures per hour. The unreliability F(t) is the probability that a system (or component) fails in a time interval [0, t]. Considering a constant failure rate λ, the unreliability F(t) is given by F(t) = 1 − e−λt

Such that cpi is the accumulated cost of testing all m components in the cutset i as in (11) m ∑ tj (11) cpi =

(8)

In this work, evaluations are done considering a time interval of 1 year (8760 hours): Qs is the unreliability of system s; qcutseti is the unreliability of cutset i; and Pr{c} 567

However, DC does not consider the ordering of components in BDD. Hence, BDDs with same boolean expression will return the same DC. In order to capture the different orders a change is proposed to compute BDD cost. Given a BDD b with n paths from the root to a leaf ONE and m components in path i, the diagnostic cost DC’(b) is given by [m(i) ] n 1 ∑ ∏ DC’(b) =

Qs

·

i=1

j=1

[wj · P r{cj } + (1 − wj ) · (1 − P r{cj })] ·cpi

(12)

where wj = 1 if component cj comes from a 1-edge and wj = 0 otherwise. DC’ is similar to DC in (10), but it is computed over all BDD paths ending in a leaf ONE. 3. PROBLEM DESCRIPTION Modern vehicles have lots of electronic devices performing different tasks such as active control, safety, comfort and entertainment. These tasks are executed by several Electronic Control Units (ECU) which are microprocessor units that take care of a particular vehicles’ subsystem. Moreover, they monitor operating conditions which can be used to alert malfunctioning. In digital systems, a fault is an anomalous physical condition of a component. An error is a manifestation of a fault in the system (Nelson, 1990). Based on observed conditions (errors), vehicles’ ECUs are able to generate online alerts to the driver as well as store numerical fault codes (DTCs) to be used in offine vehicle diagnostic. These DTCs are designed to consider several components and subsystems of different manufacturers built for different vehicles’ models and configurations. In automotive systems a particular DTC can be considered as root of both FT and BDD trees. For FT tree, it maps a system fault (DTC) into a combination of its possible causes. For BDD tree, it represents a sequence of tests

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Fig. 4. Merging strategy for diagnostic. Fig. 3. Diagnostic in automotive systems. that must be performed in order to identify the faulty components which caused the observed condition (DTC). The automotive diagnostic is guided by BDD information. It is important to understand the relation between FTs and BDDs, as BDDs are usually built from corresponding fault trees by a previous fault analysis during vehicle development cycles. Fault analysis and algorithms for generating BDDs from fault trees can be found in Rauzy (1993); Dutuit and Rauzy (2013). The general framework of diagnostic in automotive systems is depicted in Fig. 3. Firstly, a fault generates an observed error in the system. The error is caught by an ECU that trigger a corresponding DTC. However, mainly due to the great complexity of vehicle electronics, the observed error could have several causes. Hence, a diagnostic procedure is necessary. The diagnostic consists of finding the root cause of an error (DTC) by performing tests on possible faulty components. The test sequence is encoded into a corresponding BDD and carried out by specialized technicians using tools and diagnostic data during maintenance. Thus, the diagnostic task determines the fault component(s) of the system to be repaired or replaced. Definition 14. (Root cause). When multiple DTCs are triggered, the root cause of a system fail is the set of components that caused the active DTCs, i. e., it is the set of faulty components. Replacing or repairing those components, the system should resume to normal (nonfault) operation. A diagnostic procedure with one active DTC is straightforward. The technician executes a sequence of tests presented in a single BDD. However, a strategy of diagnostic in presence of several active DTCs is not easy, particularly when there are BDDs with common components which could cause multiple activations. In this case, the technician has no clue (besides his/her own experience) to decide which sequence of BDDs has to be taken. In addition, 568

depending on which sequence of BDDs is chosen, several tests can be made unnecessarily. Our work presents a strategy for combining multiple BDDs into a single one in order to improve the diagnostic procedure in presence of multiple triggered DTCs. 4. COMBINING MULTIPLE DTCS We propose a merging approach based on the current active and non-active BDDs when multiple DTCs are triggered. Definition 15. (Diagnostic scenario). A diagnostic scenario is defined by two disjoint sets of active (ACT ) and nonactive (ACT ) DTCs. The merging strategy considers the information of which DTCs are active and which ones are not to create a new BDD structure. For example, an automotive system with n predefined DTCs (each one corresponding to a particular BDD) has 2n possible different combinations of active and nonactive DTCs, or 2n possible diagnostic scenarios. Although each scenario could be stored for diagnostic, they grow exponentially and thus impose limits to the available memory. Our strategy builds a new sequence of tests dynamically for each particular diagnostic scenario. In this case, only n BDDs have to be stored. The proposed merging strategy is depicted in Fig. 4. Shadow blocks represent procedure blocks in which an operation is done. Standard blocks represent procedure outputs. The algorithm starts by traversing recursively each BDD and evaluating its solutions. The recursive approach is based on an algorithm presented in Rauzy (1993) that utilizes the Shannon decomposition representation of BDDs.

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Each individual cutset in a single BDD is a possible root cause of a single DTC. It is represented by a minterm in a boolean expression in disjunctive normal form (DNF). Hence, the set of all cutsets is a complete description of DNF. As the automotive industry encodes sequences of tests using BDDs, they are our basic input data for diagnostic.

There are approaches in the literature that compute the minimal BDD (with the minimum number of nodes). But they do not take into account the importance or cost of each test. Our approach on the other hand attempts to minimize the size of the final BDD using a measure of test importance and cost represented by CDIF to define a variable ordering.

Given a DNF representation for each BDD, the second step is to compute the logical conjunction on these boolean expressions in a particular manner. This evaluation is done using an approach introduced by Assaf and Dugan (2008). It defines two types of information used to construct the model from which the final BDD will be built. The first type is collected from sensors that monitors conditions and states of the system. The information of several sensors is combined to construct such model. However, additional system information may be available through further sources. This second type of information, known as evidences, is used to reduce the number of suspected root causes on consideration. This information is used to refine the previous model.

The choice of CDIF is based on its meaning for diagnostic. DIF is a measure associated with a component that quantifies its probability of failure considering a diagnostic scenario. In other words, given a particular set of active DTCs (encoded by the minimal model), it is possible to infer from DIF values which causes (faulty components) are more likely to occur given the current diagnostic scenario. CDIF is derived from DIF, but test costs are also taken into account. Therefore, CDIF is a tradeoff between DIF and test cost.

DTCs in the ACT set are those who actually were triggered and hold some faulty condition through its cutsets. The conjunction of DTCs in ACT yields to a model of the current diagnostic scenario named ACT-model. This model contains the boolean expressions of the BDDs of only active DTCs. A BDD is then built from ACT-model. In addition, this model can be further simplified by using the ACT set information. This information reduces the number of possible root causes, cutting down the number of cutsets examined in diagnostic. In our proposal, this information is considered as the evidences. The getCUE algorithm presented by Assaf and Dugan (2008) is used to combine the ACT-model with evidences, or ACT and ACT sets respectively. The evidences are included in the ACT-model one by one increasing the knowledge of diagnosis and reducing both the possible causes and the necessary tests. Essentially it performs the conjunction of the boolean expressions of each DTC maintaining its state (active or non-active). This step with further logical minimization results in a set of (minimal) candidates for the root cause of system fail, since any of its members is capable of generate all active DTCs. This set can be viewed as the boolean expression that represents (models) the current diagnostic scenario, in which both ACT and ACT information were used. In this work, this boolean expression is called Minimal model. Definition 16. (Minimal model). The minimal model is the boolean expression that encodes the minimal cutsets of the final BDD. These cutsets trigger all active DTCs and do not trigger any non-active DTC. The minimal model represents the diagnostic scenario, from which the final BDD is synthesized. However, a variable ordering should be defined to build a BDD from its corresponding boolean expression. In general, the chosen variable ordering has a significant impact on the BDD size and thus in the number of diagnostic testes performed.

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By gathering the minimal model and the variable ordering provided by CDIF, the final BDD is built such that components with higher CDIF have priority to be tested. Rauzy (1993) presents an algorithm that creates a BDD from its minimal cutsets using a prefixed ordering. Thus the final BDD encodes the minimal model to be used for diagnostic when multiple faults occur. Algorithm 1 presents the proposed merging algorithm to deal with multiple faults by computing the final BDD. This BDD contains components that cause the active DTCs and do not contain any component that could activate nonactive DTCs. Algorithm 1 Combining multiple faults into a single BDD Require: ACT BDDs: DTC(1), DTC(2), ... , DTC(n); ACT BDDs: DT C(1), DT C(2), ... , DT C(m) Ensure: final BDD: DTCm function createACT-model(ACT) for i = 1 to n do Compute minimal cutsets MC(i) for DTC(i) ∈ ACT end for ACT-model ← MC(1) for i = 2 to n do ACT-model ← ACT-model ∧ MC(i) end for end function function getCUE(ACT-model, ACT ) // (see Assaf and Dugan (2008)) Minimal model ← ACT-model for i = 1 to m do Minimal model ← Minimal model ∧ DT C(i) end for end function Compute CDIF for all c ∈ var(Minimal model) Built DTCm by ordering variables according to CDIF

However, an interactive procedure is made in a practical diagnostic as shown in Fig. 5. After each iteration, input BDDs are updated to take into account fixed components (replaced or repaired). This is done by setting the corresponding variable to ZERO in the boolean expressions.

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Fig. 5. The proposed diagnostic approach using a merging strategy for several root causes. Hence, the number of DTCs should decrease as faulty components are identified and fixed. The diagnostic ends by clearing all DTCs which means that all faulty components were fixed. This means that all components are good and no more tests are necessary.

Fig. 6. BDDs for example 17. Table 2. Diagnostic cost for each BDD. BDD DT C2 DT C3 DT C4 Total

5. ILLUSTRATIVE EXAMPLE Example 17. Suppose a system composed by seven components c1 , . . . , c7 and four diagnostic trouble codes DT C1 , . . . , DT C4 which are diagnosed by means of four boolean expressions s1 , s2 , . . . , s4 whose BDDs are depicted in Fig. 6. Probabilities of fail and test costs of components are given in Table 1. Also suppose that three DTCs are triggered: DT C2 , DT C3 and DT C4 . The diagnostic scenario is then defined by ACT = {DT C2 , DT C3 , DT C4 } and ACT = {DT C1 }. Table 1. Probabilities of fail and test costs of components. Component Pr{c} tc ($)

c1 0.002 200

c2 0.1 1000

c3 0.05 300

c4 0.2 250

c5 0.04 500

c6 0.05 750

Cost ($) 1336.2 560.00 1551.7 3447.9

c2 c4 c6 . Furthermore, the evidences are included in the model by using ACT . The resulting boolean expression is then sm = c2 c3 c4 + c2 c4 c6 . Such model is a more compact representation of the diagnostic scenario because it encodes both ACT and ACT sets. CDIF values for each variable in sm are necessary to define the sequence of tests to be applied in possible fault components. They are shown in Table 3. Table 3. CDIF for possible fault components.

c7 0.01 500

Component CDIF

The corresponding boolean expressions are s1 = c1 + c3 c7 + c2 c5 c6 , s2 = c1 + c2 c4 , s3 = c3 + c4 + c5 , and s4 = c1 + c2 c3 + c2 c6 . The standard approach used in industry is to consider each BDD of Fig. 6 independently. The diagnostic cost of active DTCs according to (12) is shown in Table 2. The logical conjunction and minimization of the individual minimal cuts results in sact = c1 c3 + c1 c4 + c1 c5 + c2 c3 c4 + 570

c2 1 · 10−3

c3 1.75 · 10−3

c4 4 · 10−3

c6 7 · 10−4

A test with higher CDIF appear first in the final BDD as shown in Fig. 7. Note that component c1 is not in sm as it is not faulty for sure (otherwise, DT C1 would be active too). By using the standard diagnostic approach, c1 might be unnecessarily tested. The diagnostic cost for the BDD of Fig. 7 is $1868.4 which corresponds to 54% of the cost using the standard diagnostic approach.

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Fig. 7. Resultant BDD. 6. CONCLUSION Guided diagnostic tools in automotive industry usually recovery hundreds of DTCs logged into electronic devices due to active system fails. Each DTC has a corresponding BDD with possible common faulty components which makes diagnostics a difficult task. This work has proposed a strategy for combining multiple BDDs into a single one by considering active and non-active DTCs. The results have been evaluated in terms of the total diagnostic cost using a slightly different metric found in the literature. They have shown that a significant decrease in cost is obtained by avoiding tests of components that do not affect a particular diagnostic scenario. Future work considers to implement this approach in a guided diagnostic tool. ACKNOWLEDGEMENTS The authors thank F. Mori from Volvo do Brasil for his helpful technical support. REFERENCES Assaf, T. and Dugan, J.B. (2008). Diagnosis based on reliability analysis using monitors and sensors. Reliability Engineering and System Safety, 93, 509–521. Dutuit, Y. and Rauzy, A. (2013). Importance factors of coherent systems: a review. Proc. of the Inst. of Mechanical Engineers, Part O: Journal of Risk and Reliability, 228(3), 313–323. Nelson, V.P. (1990). Fault-tolerant computing: fundamental concepts. Computer, 23(7), 19–25. Rauzy, A. (1993). New algorithms for fault trees analysis. Reliability Engineering and System Safety, 40(3), 203– 211. Reay, K.A. and Andrews, J.D. (2002). A fault tree analysis strategy using binary decision diagrams. Reliability Engineering and System Safety, 78, 45–56. Remenyte, R. and Andrews, J.D. (2006). A simple component connection approach for fault tree conversion to binary decision diagram. In Proc. of the First Int. Conf. on Availability, Reliability and Security, 1–6. Suwatthikul, J. (2010). Fault detection and diagnosis for in-vehicle networks. In W. Zhang (ed.), Fault Detection, chapter 13, 283–306. InTech. 571