Equipment failure mode predetermination and residual life prediction coupling system and method

An equipment failure mode predetermination and residual life prediction coupling system, comprising an equipment maintenance network and an equipment maintenance decision-making platform which are in communication connection with each other, wherein the equipment maintenance network is used for determining a failure mode and predicting a residual life, and the equipment maintenance decision-making platform is used for receiving the failure mode and residual life of equipment and transmitting the failure mode and residual life of the equipment to operation and maintenance staff;

• the equipment maintenance network comprises equipment nodes, sensor sets, abnormal state recognition modules and an equipment failure mode predetermination and residual life prediction coupling module;
• each of the sensor sets comprises a plurality of sensors used for acquiring equipment health information of the corresponding equipment node;
• each of the abnormal state recognition modules is used for receiving and caching the equipment health information and recognizing a time of occurrence of an exception of equipment to be detected;
• the equipment failure mode predetermination and residual life prediction coupling module is used for determining the failure mode and predicting the residual life according to the time of occurrence of the exception, and transmitting the failure mode and the residual life to the equipment maintenance decision-making platform;
• the equipment failure mode predetermination and residual life prediction coupling system is implemented by an equipment failure mode predetermination and residual life prediction coupling method, which comprises the following steps:
• S1:
• acquiring, by the corresponding sensors, the equipment health information of the equipment to be detected;
• S2:
• receiving and caching the equipment health information and recognizing the time of occurrence of the exception of the equipment to be detected, by the corresponding abnormal state recognition module;
• S3:
• according to the time of occurrence of the exception of the equipment to be detected, determining the failure mode and predicting the residual life by the equipment failure mode predetermination and residual life prediction coupling module;
• and
• S4:
• transmitting the failure mode and the residual life to the equipment maintenance decision-making platform;
• S3 comprises the following sub-steps:
• S31:
• extracting, a temporal sample, of the equipment failure mode predetermination and residual life prediction coupling module;
• S32:
• calculating, by a probabilistic graphical model, a cumulative failure occurrence probability of the temporal sample;
• S33:
• according to the cumulative failure occurrence probability of the temporal sample, calculating an empirical distribution function of each probabilistic graphical model fragment, and taking a time and failure mode corresponding to a maximum value of the empirical distribution function as an equipment residual life predicted value and a failure mode predicted value respectively;
• S34:
• determining, by a DKW inequation, a confidence interval of the equipment residual life predicted value, and calculating, by an edge density function, a variance of the failure mode predicted value;
• and
• S35:
• taking the equipment residual life predicted value, the failure mode predicted value, the confidence interval of the equipment residual life predicted value and the variance of the failure mode predicted value as prediction results of the equipment failure mode predetermination and residual life prediction coupling module;
• in S31, the temporal sample xpq is expressed as xpq={xpq(1), . . . , xpq(Tgqc−1),xpq(Tpqc)};
• where, xpq(1), . . . , xpq(Tgqc−1), xpq(Tpqc) denote health information fragments of Tpqc equipment nodes q of an equipment type p periodically collected at a time Tpc;
• in S32, a specific method for calculating the cumulative failure occurrence probability of the temporal sample comprises:
• moving rightwards, by the probabilistic graphical model, the probabilistic graphical model fragment corresponding to each element in a probabilistic graphical model set by one time slice, calculating a distance from a right end of each probabilistic graphical model fragment to a right end of the corresponding element and an accumulative occurrence probability of each failure mode until the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element is zero, and determining the accumulative failure occurrence probability;
• wherein, the probabilistic graphical model set is expressed as {Gp1, Gp2, . . . , GpKp}, where Gp1, Gp2, . . . , GpKp denote probabilistic graphical models corresponding to Kp failure modes of the equipment type p;
• the accumulative occurrence probability F(lpk,k|xpq) of each failure mode is calculated by:
• F
• ⁡
• (
• l
• pk
• ,
• k
• ❘
• x
• pq
• )
• =
• Pr
• ⁡
• (
• RUL
• <
• l
• pq
• ,
• k
• ,
• x
• pq
• )
• /
• Pr
• ⁡
• (
• x
• pq
• )
• where, lpk denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(⋅) denotes a probability function, and k denotes each failure mode of the equipment type p;
• in S33, the empirical distribution function Pr(RUL=lpk,k|xpq) of each probabilistic graphical model fragment is expressed as:
• Pr
• ⁡
• (
• RUL
• =
• l
• pk
• ,
• k
• ❘
• x
• pq
• )
• =
• F
• ⁡
• (
• l
• pk
• +
• 1
• ,
• k
• ❘
• x
• pq
• )
• -
• F
• ⁡
• (
• l
• pk
• ,
• k
• ❘
• x
• pq
• )
• ,
• l
• pk
• =
• -
• T
• p
• +
• T
• p
• c
• -
• 1
• ,
• -
• T
• p
• +
• T
• p
• c
• ,
• …
• ,
• 0
• where, lpk denotes the distance from the right end of each probabilistic graphical model fragment to the right end of the corresponding element, xpq denotes the temporal sample, RUL denotes the residual life, Pr(⋅) denotes the probability function, k denotes each failure mode of the equipment type p, F(⋅) denotes the probability function, TP denotes the number of time slices in the probabilistic graphical model, and Tpc denotes a periodical extraction time.