Equipment state evaluation method based on LS (Least Squares) reduction method

Abstract

The invention discloses an equipment state evaluation method based on an LS reduction method, and the method comprises the steps: obtaining related parameters of transmission parts of metallurgical equipment, and constructing a high-dimensional equation set of each related parameter according to the related parameters; based on the state influence factors of the metallurgical equipment transmission part and the high-dimensional equation set of each related parameter, constructing a high-dimensional joint equation; performing high-dimensional equation dimensionality reduction on the high-dimensional joint equation by using an LS method to obtain an evaluator for evaluating the state of the metallurgical equipment transmission part by using the relevant parameters of the metallurgical equipment transmission part; performing modeling and operation on the evaluator by using Sympy to obtain an evaluation module; relevant parameters of the metallurgical equipment transmission parts are obtained from the IOT platform, the relevant parameters obtained from the IOT platform are input into the evaluation module to be evaluated, and the running states of the metallurgical equipment transmission parts are obtained. According to the equipment state evaluation method, the initiated and evolved faults in the operation process of the transmission parts of the metallurgical equipment can be recognized in time.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment status evaluation, and in particular to an equipment status evaluation method based on LS reduction method. Background Art

[0002] With the development of modern science and technology, the integration of equipment is becoming higher and higher, and it is becoming more and more complex, which increases the possibility of equipment failure and the difficulty of equipment maintenance. Failure of a single component can cause the entire system to fail, causing equipment downtime, resulting in significant economic losses, and in serious cases, production accidents. If the faults that arise and evolve during the operation of each component of the equipment can be accurately and timely identified, it is possible to perform necessary maintenance on the equipment before the failure occurs, so as to prevent it from happening. Therefore, the research on equipment health assessment and diagnosis methods is of great significance.

[0003] In recent years, with the in-depth research on fault mechanisms and the vigorous development of sensor technology, information processing technology, and computer technology, artificial intelligence technology has been widely used in the field of mechanical condition assessment and fault diagnosis, promoting the development of intelligent assessment and diagnosis technology to a higher level.

[0004] The main research on mechanical equipment fault diagnosis methods can be divided into three aspects: first, the selection of measurement points and the acquisition of fault information; second, the research on signal processing and feature extraction methods; third, the comprehensive characteristic information for fault diagnosis. Among them, how to analyze the factors affecting the equipment operation status based on various equipment parameters is the key to mechanical equipment status assessment and fault diagnosis technology. Summary of the invention

[0005] The purpose of this application is to provide an equipment status evaluation method based on the LS reduction method to address the technical defects existing in the prior art.

[0006] The technical solution adopted to achieve the purpose of this application is:

[0007] A device state assessment method based on LS reduction method comprises the following steps:

[0008] Step 1, obtaining relevant parameters of transmission components of each metallurgical equipment, and constructing a high-dimensional equation group of each relevant parameter according to the relevant parameters;

[0009] Step 2, constructing a high-dimensional joint equation based on the state influencing factors of the transmission parts of the metallurgical equipment and the high-dimensional equation group of each related parameter;

[0010] Step 3, using the LS method to reduce the dimension of the high-dimensional joint equation in step 2, and obtaining an evaluator for evaluating the state of the metallurgical equipment transmission component by using the relevant parameters of the metallurgical equipment transmission component;

[0011] Step 4, use Sympy to model and calculate the evaluator in step 3 to obtain an evaluation module;

[0012] Step 5, obtaining relevant parameters of the transmission components of each metallurgical equipment from the IOT platform, and inputting the relevant parameters obtained from the IOT platform into the evaluation module of step 4 for evaluation to obtain the operating status of the transmission components of each metallurgical equipment.

[0013] In the above technical solution, the expression of the high-dimensional equation group of the relevant parameters is as follows:

[0014] F(J)=(J1, J2, J3, J4...);

[0015] F(α)=(α1,α2,α3,α4...);

[0016] F(K)=(K1, K2, K3, K4...);

[0017] F(X)=(X1,X2,X3,X4...);

[0018] F(T)=(T1, T2, T3, T4...);

[0019] Wherein, F(J) represents the high-dimensional equation group of moment of inertia, F(α) represents the high-dimensional equation group of angular velocity, F(K) represents the high-dimensional equation group of equipment stiffness, F(X) represents the high-dimensional equation group of vibration sensor data, F(T) represents the high-dimensional equation group of detection temperature, J represents the moment of inertia of the transmission parts of metallurgical equipment, α represents the angular velocity of the transmission parts of metallurgical equipment, K represents the equipment stiffness of the transmission parts of metallurgical equipment, X represents the vibration sensor data of the transmission parts of metallurgical equipment, and T represents the detection temperature of the transmission parts of metallurgical equipment.

[0020] In the above technical solution, the high-dimensional joint equation is expressed as follows:

[0021] G(a,b,c,d,e,f,g,h,i,j)=H(F(J),F(α),F(K),F(X),F(T));

[0022] In the formula, a represents the imbalance of the transmission parts of the metallurgical equipment, b represents the misalignment of the transmission parts of the metallurgical equipment, c represents the looseness of the transmission parts of the metallurgical equipment, d represents the bearing of the transmission parts of the metallurgical equipment, e represents the gear of the transmission parts of the metallurgical equipment, f represents the vibration of the transmission parts of the metallurgical equipment, g represents the temperature of the transmission parts of the metallurgical equipment, h represents the rotor eccentricity of the transmission parts of the metallurgical equipment, i represents the motor factor of the transmission parts of the metallurgical equipment, j represents the resonance factor of the transmission parts of the metallurgical equipment, F(J) represents the high-dimensional equation group of the moment of inertia, F(α) represents the high-dimensional equation group of the angular velocity, F(K) represents the high-dimensional equation group of the equipment stiffness, F(X) represents the high-dimensional equation group of the vibration sensor data, and F(T) represents the high-dimensional equation group of the detected temperature.

[0023] In the above technical solution, the evaluator expressions of the various state influencing factors are as follows:

[0024] P(a)=ε a (J,α,K,X,T);

[0025] P(b)=ε b (J,α,K,X,T);

[0026] P(c)=ε c (J,α,K,X,T);

[0027] P(d)=ε d (J,α,K,X,T);

[0028] P(e)=ε e (J,α,K,X,T);

[0029] P(f)=ε f (J,α,K,X,T);

[0030] P(g)=ε g (J,α,K,X,T);

[0031] P(h)=ε h (J,α,K,X,T);

[0032] P(i)=ε i (J,α,K,X,T);

[0033] P(j)=ε j (J,α,K,X,T);

[0034] Wherein, P(a) represents an evaluator for imbalance evaluation using relevant parameters of metallurgical equipment transmission components, P(b) represents an evaluator for misalignment evaluation using relevant parameters of metallurgical equipment transmission components, P(c) represents an evaluator for looseness evaluation using relevant parameters of metallurgical equipment transmission components, P(d) represents an evaluator for bearing evaluation using relevant parameters of metallurgical equipment transmission components, P(e) represents an evaluator for gear evaluation using relevant parameters of metallurgical equipment transmission components, P(f) represents an evaluator for vibration evaluation using relevant parameters of metallurgical equipment transmission components, P (g) represents an evaluator for temperature evaluation using relevant parameters of metallurgical equipment transmission components, P(h) represents an evaluator for rotor eccentricity evaluation using relevant parameters of metallurgical equipment transmission components, P(i) represents an evaluator for motor factor evaluation using relevant parameters of metallurgical equipment transmission components, P(j) represents an evaluator for resonance factor evaluation using relevant parameters of metallurgical equipment transmission components, a, b, c, d, e, f, g, h, i, j respectively represent state influencing factors of metallurgical equipment transmission components, and J, α, K, X, T respectively represent relevant parameters of metallurgical equipment transmission components.

[0035] The beneficial effects of the present invention are as follows:

[0036] The equipment status assessment method of the present invention can analyze the status influencing factors of each metallurgical equipment transmission component according to the relevant parameters of each metallurgical equipment transmission component, obtain the operating status of each metallurgical equipment transmission component, and can timely identify the faults that arise and evolve during the operation of each metallurgical equipment transmission component. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 Flow chart of the device status evaluation method according to the present invention DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with specific embodiments.

[0040] A method for evaluating equipment status based on LS reduction method, see Figure 1 , including the following steps:

[0041] Step 1: Obtain relevant parameters of transmission components of each metallurgical equipment, and construct a high-dimensional equation group of each relevant parameter according to the relevant parameters.

[0042] The transmission components of the metallurgical equipment include a rotor, a coupling, a reducer, a main coupling, a gear frame, a connecting shaft and a rolling roller, etc. The relevant parameters include moment of inertia, angular velocity, equipment stiffness, vibration sensor data and detection temperature.

[0043] The expressions of the high-dimensional equations of the relevant parameters are as follows:

[0044] F(J)=(J1, J2, J3, J4...);

[0045] F(α)=(α1,α2,α3,α4...);

[0046] F(K)=(K1, K2, K3, K4...);

[0047] F(X)=(X1,X2,X3,X4...);

[0048] F(T)=(T1, T2, T3, T4...);

[0049] Wherein, F(J) represents the high-dimensional equation group of moment of inertia, F(α) represents the high-dimensional equation group of angular velocity, F(K) represents the high-dimensional equation group of equipment stiffness, F(X) represents the high-dimensional equation group of vibration sensor data, F(T) represents the high-dimensional equation group of detection temperature, J represents the moment of inertia of the transmission parts of metallurgical equipment, α represents the angular velocity of the transmission parts of metallurgical equipment, K represents the equipment stiffness of the transmission parts of metallurgical equipment, X represents the vibration sensor data of the transmission parts of metallurgical equipment, and T represents the detection temperature of the transmission parts of metallurgical equipment.

[0050] Step 2: construct a high-dimensional joint equation based on the state influencing factors of the transmission components of the metallurgical equipment and the high-dimensional equation group of various related parameters.

[0051] The factors affecting the state of the transmission parts of the metallurgical equipment include imbalance, misalignment, looseness, bearings, gears, vibration, temperature, rotor eccentricity, motor factors and resonance factors.

[0052] The high-dimensional joint equation expression is as follows:

[0053] G(a,b,c,d,e,f,g,h,i,j)=H(F(J),F(α),F(K),F(X),F(T));

[0054] In the formula, a represents the imbalance of the transmission parts of the metallurgical equipment, b represents the misalignment of the transmission parts of the metallurgical equipment, c represents the looseness of the transmission parts of the metallurgical equipment, d represents the bearing of the transmission parts of the metallurgical equipment, e represents the gear of the transmission parts of the metallurgical equipment, f represents the vibration of the transmission parts of the metallurgical equipment, g represents the temperature of the transmission parts of the metallurgical equipment, h represents the rotor eccentricity of the transmission parts of the metallurgical equipment, i represents the motor factor of the transmission parts of the metallurgical equipment, j represents the resonance factor of the transmission parts of the metallurgical equipment, F(J) represents the high-dimensional equation group of the moment of inertia, F(α) represents the high-dimensional equation group of the angular velocity, F(K) represents the high-dimensional equation group of the equipment stiffness, F(X) represents the high-dimensional equation group of the vibration sensor data, and F(T) represents the high-dimensional equation group of the detected temperature.

[0055] The equilibrium point of the high-dimensional system satisfies the condition G(X)=0, and X represents the equilibrium point of the high-dimensional system.

[0056] Step 3, using the LS (Lyapunov Schmidt) method to reduce the high-dimensional joint equation in step 2, and obtain an evaluator for evaluating the state of the metallurgical equipment transmission components using the relevant parameters of the metallurgical equipment transmission components.

[0057] The evaluator expressions of the various state influencing factors are as follows:

[0058] P(a)=ε a (J,α,K,X,T);

[0059] P(b)=ε b (J,α,K,X,T);

[0060] P(c)=ε c (J,α,K,X,T);

[0061] P(d)=ε d (J,α,K,X,T);

[0062] P(e)=ε e (J,α,K,X,T);

[0063] P(f)=ε f (J,α,K,X,T);

[0064] P(g)=ε g (J,α,K,X,T);

[0065] P(h)=ε h (J,α,K,X,T);

[0066] P(i)=ε i (J,α,K,X,T);

[0067] P(j)=ε j (J,α,K,X,T);

[0068] Wherein, P(a) represents an evaluator for imbalance evaluation using relevant parameters of metallurgical equipment transmission components, P(b) represents an evaluator for misalignment evaluation using relevant parameters of metallurgical equipment transmission components, P(c) represents an evaluator for looseness evaluation using relevant parameters of metallurgical equipment transmission components, P(d) represents an evaluator for bearing evaluation using relevant parameters of metallurgical equipment transmission components, P(e) represents an evaluator for gear evaluation using relevant parameters of metallurgical equipment transmission components, P(f) represents an evaluator for vibration evaluation using relevant parameters of metallurgical equipment transmission components, P (g) represents an evaluator for temperature evaluation using relevant parameters of metallurgical equipment transmission components, P(h) represents an evaluator for rotor eccentricity evaluation using relevant parameters of metallurgical equipment transmission components, P(i) represents an evaluator for motor factor evaluation using relevant parameters of metallurgical equipment transmission components, P(j) represents an evaluator for resonance factor evaluation using relevant parameters of metallurgical equipment transmission components, a, b, c, d, e, f, g, h, i, j respectively represent state influencing factors of metallurgical equipment transmission components, and J, α, K, X, T respectively represent relevant parameters of metallurgical equipment transmission components.

[0069] Step 4: Use Sympy (a python algebraic symbol library) to model and calculate the evaluator in step 3 to obtain an evaluation module.

[0070] Step 5, obtaining relevant parameters of the transmission components of each metallurgical equipment from the IOT platform, and inputting the relevant parameters obtained from the IOT platform into the evaluation module of step 4 for evaluation to obtain the operating status of the transmission components of each metallurgical equipment.

[0071] The relevant parameters obtained from the IOT platform may be automatically (event-triggered or periodically) or manually input into the evaluation module of step 4.

[0072] The above is only a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A device state assessment method based on LS reduction method, characterized in that: The following steps are involved: Step 1, obtaining relevant parameters of transmission components of each metallurgical equipment, and constructing a high-dimensional equation group of each relevant parameter according to the relevant parameters; Step 2, constructing a high-dimensional joint equation based on the state influencing factors of the transmission parts of the metallurgical equipment and the high-dimensional equation group of each related parameter; Step 3, using the LS method to reduce the dimension of the high-dimensional joint equation in step 2, and obtaining an evaluator for evaluating the state of the metallurgical equipment transmission component by using the relevant parameters of the metallurgical equipment transmission component; Step 4, use Sympy to model and calculate the evaluator in step 3 to obtain an evaluation module; Step 5, obtaining relevant parameters of the transmission components of each metallurgical equipment from the IOT platform, and inputting the relevant parameters obtained from the IOT platform into the evaluation module of step 4 for evaluation to obtain the operating status of the transmission components of each metallurgical equipment.

2. The device status assessment method according to claim 1, characterized in that: The expressions of the high-dimensional equations of the relevant parameters are as follows: F(J)=(J1, J2, J3, J4...); F(α)＝(α1，α2，α3，α4...)； F(K)=(K1, K2, K3, K4...); F(X)=(X1,X2,X3,X4...); F(T)=(T1, T2, T3, T4...); Wherein, F(J) represents the high-dimensional equation group of moment of inertia, F(α) represents the high-dimensional equation group of angular velocity, F(K) represents the high-dimensional equation group of equipment stiffness, F(X) represents the high-dimensional equation group of vibration sensor data, F(T) represents the high-dimensional equation group of detection temperature, J represents the moment of inertia of the transmission parts of metallurgical equipment, α represents the angular velocity of the transmission parts of metallurgical equipment, K represents the equipment stiffness of the transmission parts of metallurgical equipment, X represents the vibration sensor data of the transmission parts of metallurgical equipment, and T represents the detection temperature of the transmission parts of metallurgical equipment.

3. The device status evaluation method according to claim 1, characterized in that: The high-dimensional joint equation expression is as follows: G(a,b,c,d,e,f,g,h,i,j)=H(F(J),F(α),F(K),F(X),F(T)); In the formula, a represents the imbalance of the transmission parts of the metallurgical equipment, b represents the misalignment of the transmission parts of the metallurgical equipment, c represents the looseness of the transmission parts of the metallurgical equipment, d represents the bearing of the transmission parts of the metallurgical equipment, e represents the gear of the transmission parts of the metallurgical equipment, f represents the vibration of the transmission parts of the metallurgical equipment, g represents the temperature of the transmission parts of the metallurgical equipment, h represents the rotor eccentricity of the transmission parts of the metallurgical equipment, i represents the motor factor of the transmission parts of the metallurgical equipment, j represents the resonance factor of the transmission parts of the metallurgical equipment, F(J) represents the high-dimensional equation group of the moment of inertia, F(α) represents the high-dimensional equation group of the angular velocity, F(K) represents the high-dimensional equation group of the equipment stiffness, F(X) represents the high-dimensional equation group of the vibration sensor data, and F(T) represents the high-dimensional equation group of the detected temperature.

4. The device status evaluation method according to claim 1, characterized in that: The evaluator expressions of the various state influencing factors are as follows: P(a)=ε a (J,a,K,X,T); P(b) is b (J,α,K,X,T) P(c)=ε c (J,α,K,X,T); P(d)=ε d (J,α,K,X,T); P(e)=e e (J,a,K,X,T); P(f)=ε f (J,α,K,X,T); P(g) is g (J,α,K,X,T) P(h)=ε h (J,α,K,X,T); P(i)=e i (J,a,K,X,T); P(j)=ε j (J,α,K,X,T)? Wherein, P(a) represents an evaluator for imbalance evaluation using relevant parameters of metallurgical equipment transmission components, P(b) represents an evaluator for misalignment evaluation using relevant parameters of metallurgical equipment transmission components, P(c) represents an evaluator for looseness evaluation using relevant parameters of metallurgical equipment transmission components, P(d) represents an evaluator for bearing evaluation using relevant parameters of metallurgical equipment transmission components, P(e) represents an evaluator for gear evaluation using relevant parameters of metallurgical equipment transmission components, P(f) represents an evaluator for vibration evaluation using relevant parameters of metallurgical equipment transmission components, P (g) represents an evaluator for temperature evaluation using relevant parameters of metallurgical equipment transmission components, P(h) represents an evaluator for rotor eccentricity evaluation using relevant parameters of metallurgical equipment transmission components, P(i) represents an evaluator for motor factor evaluation using relevant parameters of metallurgical equipment transmission components, P(j) represents an evaluator for resonance factor evaluation using relevant parameters of metallurgical equipment transmission components, a, b, c, d, e, f, g, h, i, j respectively represent state influencing factors of metallurgical equipment transmission components, and J, α, K, X, T respectively represent relevant parameters of metallurgical equipment transmission components.

Images