Malfunction early-warning method for production logistics delivery equipment

Patent metadata

Field	Value
Publication number	US-11740619-B2
Application number	US-201916968145-A
Country	US
Kind code	B2
Filing date	Oct 21, 2019
Priority date	May 31, 2019
Publication date	Aug 29, 2023
Grant date	Aug 29, 2023

Abstract

Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction.

First claim

The invention claimed is:

1. A malfunction early warning method of production logistics delivery equipment, comprising the following steps:
arranging a plurality of sensors in a production line, and collecting data including vibration acceleration signals of bearings and a speed reducer, belt displacement, of main parts of the equipment used in production are collected by a plurality of sensors; and
detecting and predicting vibration acceleration for bearings and gearboxes,
detecting and predicting current and voltage signals for servo motors, and
detecting and predicting belt displacement signals,
detecting and predicting temperature and humidity signals of major components, steps configured to be executed by one or more processors, comprising
step 1, calculating a feature vector of a historical normal operation state based on signal data obtained by the plurality of sensors, dividing normal state data into a plurality of work conditions to obtain a plurality of clustering centers, and calculating a Euclidean distance from a current state to the clustering centers so as to obtain a similarity trend;
step 2, building a historical memory matrix, optimizing parameters of an LS-SVM regression model, and calculating a residual of the current state and the regression model; and
step 3, obtaining a risk coefficient by combining the similarity trend and the residual, evaluating an equipment operation state, and making timely early warning on faults;
wherein the step 1 comprises the following specific processes:
step 1.1, performing initialization: creating two nodes with weight vectors, and a zero value of a local error;
step 1.2, inputting a vector into a neural network x, and finding two nerve cells s and t in positions closest to the x, i.e., the nodes with weight vectors w s and w t , wherein ∥w s −x∥ 2 is a node with a smallest distance value in all nodes, and ∥w t −x∥ 2 is a node with a second-smallest distance value in all nodes;
step 1.3, updating a local error of a winner nerve cell s, and adding the local error of the winner nerve cell s into a squared distance of the vector w S and the x:
E S ←E S +∥w S −x∥ 2   (1);

step 1.4, translating the winner nerve cell s and all topological neighbors thereof, wherein a direction is an input vector x, and distances equal to partial ∈ w and whole ∈ n :
w s ←w s +∈ w ·( w s −x )  (2), and

w n ←w n +∈ n ·( w n −x )  (3);

step 1.5, by using 1 as a step amplitude, increasing ages of all connections from the winner nerve cell s, and removing the connections with the ages being elder than age max ; and if a result in the nerves cells does not have more divergence margins, also removing the nerve cells;
step 1.6, if the number of current iterations is a multiple of λ, and does not reach a limit dimension of a network, inserting a new nerve cell r as follows;
step 1.7, reducing all errors of a nerve cell j by using a fraction β:
E j ←E j −E j ·β  (4); and

step 1.8, if a stop condition is not met, continuing the step 2.

2. The malfunction early warning method of production logistics delivery equipment according to claim 1 , wherein in the step 2, the improved particle swarm algorithm is used to optimize a kernel function σ and a penalty coefficient c in the LS-SVM regression model.

3. The malfunction early warning method of production logistics delivery equipment according to claim 2 , wherein the step 2 comprises the following specific processes:
step 2.1.1, building the LS-SVM regression model: introducing a Lagrangian function for solving it, and selecting a radial basis function K(x,x i )=exp(−∥x−x i ∥ 2 /2σ 2 ), wherein σ is a kernel width; and obtaining the LS-SVM regression model:

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step 2.1.2, checking whether a historical optimum adaptive degree P b meets a constraint condition or whether the number of iterations reaches the maximum, if the constraint condition is still not met, and the number of iterations is not the maximum, performing a step 2.1.3, and otherwise, mapping a result into the kernel function σ and the penalty coefficient c of the LS-SVM model; and
step 2.1.3, regulating speed and positions of particles, and regulating an inertia weight.

4. The malfunction early warning method of production logistics delivery equipment according to claim 3 , wherein in the step 2.1.3, a self-adaptative regulation inertia weight method is used to regulate the inertia weight:

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Assignees

• Univ Nanjing Aeronautics & Astronautics

Inventors

• Qian Xiaoming
• Lou Peihuang
• Wang Xinhao

Classifications

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title

• G06N3/082

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06N3/0985

  Hyperparameter optimisation; Meta-learning; Learning-to-learn · CPC title

• G06N3/09

  Supervised learning · CPC title

• G05B23/0245 Primary

  based on a qualitative model, e.g. rule based; if-then decisions · CPC title