CN106054857A - Maintenance decision tree/word vector-based fault remote diagnosis platform - Google Patents

Maintenance decision tree/word vector-based fault remote diagnosis platform Download PDF

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Publication number
CN106054857A
CN106054857A CN201610364696.5A CN201610364696A CN106054857A CN 106054857 A CN106054857 A CN 106054857A CN 201610364696 A CN201610364696 A CN 201610364696A CN 106054857 A CN106054857 A CN 106054857A
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Prior art keywords
word
cell
fault
diagnosis platform
failure
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CN201610364696.5A
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CN106054857B (en
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田雨农
张祥
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Dalian Roiland Technology Co Ltd
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Dalian Roiland Technology Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention belongs to the remote fault diagnosis technical field and relates to a maintenance decision tree/word vector-based fault remote diagnosis platform. The fault remote diagnosis platform comprises a fault information receiving unit used for receiving a fault description sentence T, a fault information analyzing unit used for carrying out word switched retrieval on cell words in a cell word library and arranging the cell words, and a fault information resolving unit used for giving a maintenance solution through the decision classification of a decision tree model. With the maintenance decision tree/word vector-based fault remote diagnosis platform of the invention adopted, the faults of equipment can be recognized remotely and quickly; effective maintenance solutions can be given accurately; detection standards are uniform, so that errors caused by manual judgment can be avoided; and a lot of manpower and resources can be saved.

Description

Remote Fault Diagnosis platform based on maintenance decision tree/term vector
Technical field
The present invention relates to fault teledianogsis technique field, particularly relate to fault based on maintenance decision tree/term vector remote Journey diagnostic platform.
Background technology
The fault diagnosis of traditional maintenance of equipment industry uses the empirical data of " hope, hear, ask, cut ".
Common diagnostic process is:
1. equipment fault lamp lights or user feels that equipment is problematic, than if any abnormal sound, have abnormal flavour, shake etc., i.e. " send out Existing problem enters shop ".
2. detect DTC and failure-description, i.e. " test problems " at maintenace point by the failure diagnostic apparatus of specialty.
3. maintenance technician is by seeing DTC and failure-description, in conjunction with service experience for many years, carries out field diagnostic and carries Go out solution, i.e. " solution judgement ".
4. spare part personnel open and singly provide concrete spare part number, repair parts line item and price of spare parts, i.e. " list opened by spare part ".
5. services advisor opens single to go to work item No., work item title, man-hour and working hour expense, i.e. " work Xiang Kaidan ".
Under the prior art, user to carry out diagnosing for equipment fault many times needs to find to ask by experience Topic.After being implicitly present in the problem needing to be solved by maintenance, user needs equipment delivery to maintenace point is gone detection event Barrier, wastes time and energy.During fault detect, maintenance technician combines experience and draws maintenance program, and provides spare part number, spare part name Claiming and price of spare parts, this process subjectivity is very strong, lacks unified standard.The most again by tested personnel and service consultant respectively to Go out different document, waste time and energy.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, it is provided that a kind of fault diagnosis platform, build one from event Barrier is described to term vector and decomposes, and sets up maintenance cell dictionary；By failure-description at the word and search of cutting of cell dictionary, according to cell The syntactic rule that word weight is set up, it is achieved again to the maintenance decision tree of maintenance solution from decision making process to decision recommendation, Realize Remote Fault Diagnosis eventually, and working hour expense and spare part expense are evaluated.
The present invention solves it and technical problem is that and take techniques below scheme to realize: a kind of Remote Fault Diagnosis platform bag Include: fault message receives unit, is used for receiving a described failure-description statement T；Fault information analysis unit, for described carefully Born of the same parents' dictionary is cut cell word described in word and search, and arranges described cell word；Fault message solves unit, for by decision tree mould The Decision Classfication of type, provides maintenance solution.
Preferably, described fault information analysis unit comprises failure-description statement resolving cell, retrieval unit, computing unit And arrangement units；Described statement resolving cell is for cutting in described cell dictionary a described failure-description statement T Word；Described retrieval unit is for retrieving in described cell dictionary；Described computing unit is for calculating each described cell word Weight；Described arrangement units is for arranging described cell word by described syntactic rule.
Preferably, described retrieval unit carries out cutting word and search.
Preferably, described computing unit calculates the method for weight of each described cell word and is: S1 calculate each described carefully The chi-square statistics amount of born of the same parents' word；S2 takes the i-th cell word that described chi-square statistics amount score value is the highest, calculates described i-th cell word The number of times occurred in jth failure-description.
Preferably, the computational methods of chi-square statistics amount score value described in described S1 step are:
Weight=round (10 × (1+lg (tfij))/(1+lg(lj)))；
Wherein tfijThe number of times occurred in jth failure-description for i-th cell word, ljLength for jth failure-description Degree.
Preferably, described fault message solution unit combines components and parts measured value and provides maintenance solution.
Advantages of the present invention and good effect be: can remotely and rapidly identify the fault of equipment, be given accurately Effect maintenance solution.Examination criteria is unified, it is to avoid the error brought due to artificial judgment.And save a large amount of manpower thing Power.
Accompanying drawing explanation
Fig. 1 is the structure syntactic rule figure of the present invention；
Fig. 2 is decision tree disaggregated model 1；
Fig. 3 is decision tree disaggregated model 2.
Detailed description of the invention
Below in conjunction with the accompanying drawings, by specific embodiment, the invention will be further described.Following example are descriptive , it not determinate, it is impossible to limit protection scope of the present invention with this.
The problem of natural language understanding is converted into Machine self-learning by being reached by natural language digitized by the present invention Problem.
(1) basis corpus is built
The method creating basis corpus is a given character string, the probability of its natural language be P (w1, w2, W3 ..., wn), w1 to wn is each cell word of the words successively.
Model based on such a it is assumed that the appearance of the n-th word is only the most relevant to above n-1 word, and with other any word The most uncorrelated, the probability of whole sentence is exactly the product of each participle probability of occurrence.
For a failure-description T, its probability calculation formula is:
P (T)=P (w1,w2,w3,…,wn)
=P (w1)×P(w2|w1)×P(w3|w1,w2)×…×P(wn|w1,w2,…,wn-1)
≈P(w1)×P(w2|w1)×P(w3|w2)…P(wn|wn-1)
(2) term vector decomposes: term vector is the statement after being cut into several cell words.Each failure-description T can table It is shown as the term vector of n dimension, T=(w1,w2,...,wn), wherein w1To wnFor each cell word comprised in each term vector.
As a example by vehicle failure:
Failure-description is:
Representing above-mentioned failure-description statement with term vector, term vector (w can be used in above-mentioned failure-description statement1,w2,w3,…, wn).Wherein, w1For camshaft, w2For position, w3For cylinder, w4For timing, w5For excessively.Choose each cell word as feature , wherein wiRepresent ith feature item.
Calculate the chi-square statistics amount of each cell word, in training set, then take the i-th cell word conduct that score value is the highest Represent word, thus find out and term vector (w1,w2,w3,…,wn) the minimum cell word of dependency.
wiThe computing formula of weight be:
Weight=round ((10 × (1+lg (tfij))/(1+lg(lj))),3)；
Wherein tfijThe number of times occurred in jth failure-description for i-th term vector, ljLength for jth failure-description Degree.Round function is rounded up to the decimal place specified, and is after arithmetic point the 3rd in above formula.
The feature of the failure-description term vector after calculating weight is expressed as:
Vector participle Weight
Camshaft 0.286
Position 0.191
Cylinder 0.048
Timing 0.048
Excessively 0.048
Embodiment 1
(1) retrieval arranging cells word:
As it is shown in figure 1, after user's input fault describes T, first failure-description T is carried out term vector and decomposes complete language Sentence is cut into multiple cell contamination.Then for each cell word, carry out cutting word and search in cell dictionary.If retrieval Arrive, then calculate the weight of this cell word；Without retrieving, then the cell word this not retrieved is stored in newly-increased industry Cell dictionary, then retrieve for remaining cell word, calculate its probability.
According to each cell word retrieved in cell dictionary and calculate weight, according to weight arranging cells from big to small Word.
(2) analysis result:
As a example by vehicle failure:
Failure-description is:
Each cell word of retrieving in cell dictionary also calculates weight, according to weight arranging cells word from big to small:
Use techniques of discriminant analysis:
The term vector sequence of failure-description T be T=(w1, w2, w3 ..., wn), in failure-description T each cell word calculate Sequence after weight be S=(s1, s2, s3 ..., sn).The computing formula of term vector classification and matching maximum of probability is: W= argmaxP(S|T)。
The syntactic rule arranged from high to low according to term vector classification and matching maximum of probability carries out classification:
Classification Rule
One-level Camshaft
Two grades Position
Three grades Timing
(4) decision tree analysis
Decision tree is set up for the result of cell word classification according to above-mentioned syntactic rule.
Disaggregated model 1
As in figure 2 it is shown, camshaft is one-level, position, changes, be adjusted to two grades, sensor, actuator, response, timing, Aerofluxus is three grades.Corresponding solution is respectively: sensor correspondence CMPS Camshaft Position Sensor, engine wiring harness and start Machine control unit, actuator correspondence engine wiring harness, control unit of engine and camshaft adjuster, timing correspondence electromotor line Bundle, control unit of engine, camshaft adjuster, camshaft stretcher and timing chain, by that analogy.The solution that block arrow is pointed to Certainly scheme represents the probability needing to change this accessory much larger than the probability changing the accessory that other thin arrows point to, and this probability is root Preset in decision-tree model according to a large amount of mantenance datas.Therefore, by failure-description " camshaft location (cylinder Row 2)-timing is the most advanced " carry out term vector decomposition and retrieve in cell dictionary, obtain camshaft, position and timing Three cell words, are computed cell word weight and according to syntactic rule, cell word are carried out arrangement and show that camshaft is positioned at one-level, Position is positioned at two grades, and timing is positioned at three grades.Through the Decision Classfication of decision-tree model, draw most possible maintenance solution For changing or maintenance camshaft stretcher.
Disaggregated model 2
As it is shown on figure 3, failure-description statement " CMPS Camshaft Position Sensor=> the insincere signal of sensor " is carried out word to Amount is decomposed and retrieves in cell dictionary, obtains camshaft, position, three cell words of sensor.Calculate for three cell words Cell word weight also carries out arrangement according to syntactic rule to cell word and show that camshaft is positioned at one-level, and position is positioned at two grades, sensing Device is positioned at three grades.In conjunction with components and parts measured value, it is judged that need the accessory changed.First check for wire and plug have contactless not Good, syringe needle bends.If it is, need to change wire and plug.If it is not, then enter next stage decision tree, check that camshaft passes Whether sensor voltage is supplied between 4.5-5.5V.If it is the signal voltage of control unit of engine is checked, if Between 4.5-5.5V.If otherwise needing to change engine wiring harness.By that analogy, the Decision Classfication through decision-tree model combines Components and parts measured value, provides maintenance solution.

Claims (6)

1. a Remote Fault Diagnosis platform, it is characterised in that including:
Fault message receives unit, is used for receiving a described failure-description statement T；
Fault information analysis unit, for cutting cell word described in word and search in described cell dictionary, and arranges described cell word；
Fault message solves unit, for by the Decision Classfication of decision-tree model, providing maintenance solution.
A kind of Remote Fault Diagnosis platform the most according to claim 1, it is characterised in that: described fault information analysis unit Comprise failure-description statement resolving cell, retrieval unit, computing unit and arrangement units；
Described statement resolving cell is for cutting word to a described failure-description statement T in described cell dictionary；
Described retrieval unit is for retrieving in described cell dictionary；
Described computing unit is for calculating the weight of each described cell word；
Described arrangement units is for arranging described cell word by described syntactic rule.
A kind of Remote Fault Diagnosis platform the most according to claim 2, it is characterised in that: described retrieval unit carries out cutting word Retrieval.
A kind of Remote Fault Diagnosis platform the most according to claim 2, it is characterised in that described computing unit calculates each The method of the weight of described cell word is:
S1 calculates the chi-square statistics amount of each described cell word；
S2 takes the i-th cell word that described chi-square statistics amount score value is the highest, calculates described i-th cell word and retouches in jth fault State the number of times of middle appearance.
A kind of Remote Fault Diagnosis platform the most according to claim 4, it is characterised in that card side's system described in described S1 step The computational methods of metering score value are:
Weight=round (10 × (1+lg (tfij))/(1+lg(lj)))；
Wherein tfijThe number of times occurred in jth failure-description for i-th cell word, ljLength for jth failure-description.
6. according to a kind of Remote Fault Diagnosis platform described in any claim in claim 1-5, it is characterised in that: described Fault message solution unit combines components and parts measured value and provides maintenance solution.
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CN107832908A (en) * 2017-09-29 2018-03-23 深圳供电局有限公司 A kind of standby redundancy needing forecasting method based on defective data
CN108154244A (en) * 2018-01-24 2018-06-12 南京天溯自动化控制系统有限公司 The O＆M methods, devices and systems of real estate power equipment
CN117590837A (en) * 2024-01-18 2024-02-23 深圳市伟创高科电子有限公司 Electric vehicle controller fault diagnosis method based on tree structure

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Cited By (4)

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Publication number Priority date Publication date Assignee Title
CN107832908A (en) * 2017-09-29 2018-03-23 深圳供电局有限公司 A kind of standby redundancy needing forecasting method based on defective data
CN108154244A (en) * 2018-01-24 2018-06-12 南京天溯自动化控制系统有限公司 The O＆M methods, devices and systems of real estate power equipment
CN117590837A (en) * 2024-01-18 2024-02-23 深圳市伟创高科电子有限公司 Electric vehicle controller fault diagnosis method based on tree structure
CN117590837B (en) * 2024-01-18 2024-03-29 深圳市伟创高科电子有限公司 Electric vehicle controller fault diagnosis method based on tree structure

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