CN101941649B - Automatic Detection and Fault Diagnosis Method of Crane Lifting State - Google Patents

Abstract

The invention relates to an automatic detection and fault diagnosis method of the lifting states of travelling vehicles. The method has the following beneficial effects: a weighing sensor is adopted to detect the weight of the materials; a current sensor is adopted to detect the current of main hook motors of the travelling vehicles; after acquiring multiple groups of samples obtained by the two sensors, travelling vehicle terminal software carries out fitting by a least square method to obtain a weight-current prediction model; in the process of travelling vehicle lifting, after median filtering on the detected weight of the materials and the corresponding current of the main hooks, intelligent analysis is carried out according to the weight-current prediction model to obtain the accurate weight information and the lifting states of the materials lifted by the travelling vehicles; and timely alarm is carried out in case the weighing sensor or the current sensor has faults, thus improving the accuracy and the real-time property of lifting state monitoring of the travelling vehicles. The automatic identification technology related in the invention can make up for the randomness andthe orderless property of the personnel operation warehouse areas and create conditions for full-automatic operation of the travelling vehicles.

Description

Automatic Detection and Fault Diagnosis Method of Crane Lifting State

technical field

The invention relates to a method for automatic detection and fault diagnosis of the hoisting state of the crane. The method can accurately record the weight of materials and the change of the hoisting state of the truck, and can perform effective fault detection on the load cell device, creating conditions for the fully automatic operation of the truck. .

Background technique

The crane is an important production tool for the material storage area to perform the tasks of production material transportation and storage. The existing driving automation is relatively low, and the weight information and lifting status of the lifted materials cannot be obtained accurately and reliably, which has become a major technical bottleneck for the fully automated operation of the driving.

A single load cell is vulnerable to damage and failure in a complex industrial environment, with poor anti-interference ability and short working life. Once damaged, the weight information will be wrong, which will bring potential danger to driving operation. Using a single current sensor to detect weight cannot provide accurate weight information due to insufficient precision. At the same time, the driving hook often has the action of trial lifting or adjusting the position of the hook when lifting materials. At this time, the signal of the sensor will jitter. It is difficult for a single sensor to judge whether to lift or lower the action for such an instantaneous signal. Therefore, the weighing device in the form of a single sensor cannot improve the automation of warehouse management due to its poor reliability and difficult identification of faults.

The motor current of the main hook of the crane has obvious different intervals when lifting and lowering materials. According to this characteristic, it can assist in judging the lifting and lowering operations. The use in conjunction with the load cell can also increase redundancy and give an effective alarm to failures.

Contents of the invention

Technical problem: In order to improve the accuracy of hoisting status information identification, increase the reliability of weight information and hoisting status identification and the maintainability of weighing devices. The invention provides a method for automatic detection and fault diagnosis of the driving and lifting state. When the weighing device is in normal operation, the weight information of the material and the driving and lifting state can be automatically judged without manual intervention, and the weighing or current sensor Report to the police in time when there is a failure.

Technical solution: The automatic detection and fault diagnosis method of the driving and lifting state of the present invention specifically includes the following steps:

Step 1: Under the off-line state of the automatic detection system of driving and lifting status, use materials of different standard weights to perform hoisting and lowering operations, and collect multiple material weights W _k and corresponding lifting current I by collecting load cells and current sensors _uk , put down the current I _dk or stabilize the current I _sk , use the least squares method to fit and obtain the weight-current estimation model of the material weight and the current under different lifting states;

Step 2: In the online actual measurement, filter the continuous current and weight information with the median value average filter method;

Step 3: Through the intelligent comparison and analysis of the continuous stable current and the current range of different lifting states under the weight under the weight-current estimation model when lifting a certain material in the actual online operation, accurately judge the lifting and lowering state of the crane , and report an error in time when the weighing or current sensor fails;

Step 4: Display weight information, lifting status, or fault alarm.

The median average filtering method removes a maximum value and a minimum value for the N consecutively sampled data, and then calculates the arithmetic mean of the N-2 data.

The material weight W _k and the hoisting current I _uk are fitted by the least squares fitting formula to obtain I′ _uk =a ₀ W _k +a ₁ , wherein a ₀ and a ₁ are fitting coefficients, and the fitting curve is the same as The maximum measurement error of the hoisting current I _uk is I _λumax , and it can be estimated that when the weight of the material is W _k , the hoisting current model range is I′ _uk ±I _λumax .

Described material weight W _k and putting down electric current I _dk obtain _I′dk =a ₂ W _k +a ₃ by least square method fitting formula fitting, wherein a ₂ and a ₃ are fitting coefficients, fitting curve and putting down The maximum measurement error of the current I _dk is I _λdmax , and it can be estimated that when the weight of the material is W _k , the range of the laying down current model is I′ _dk ±I _λdmax .

When lifting a certain material, when it is stationary in the vertical direction, the current basically does not change to I _sk , and the average current is I′ _sk after repeated measurements, and the maximum measurement error between the average current and the vertical static current I _sk is I _λsmax , it can be estimated that when the vertical direction is stationary, its current interval is I′ _sk ±I _λsmax .

In the online actual measurement process, the material weight information W ₀ and the corresponding current information I ₀ are obtained, and in the weight-current estimation model obtained by fitting, I ₀ is compared with the current interval corresponding to W ₀ in the model,

If I ₀ ∈ I′ _sk ±I _λsmax , do not process and wait for the next measurement value,

If I ₀ ∈ I′ _dk ±I _λdmax , then display the put down state,

If I ₀ falls within the hoisting current interval I′ _uk ±I _λumax , it will display the hoisting state,

If I ₀ is not in these two intervals, when I ₀ is closer to I′ _dk , and |I ₀ -I′ _dk |/I′ _dk ≤ α, where α is determined according to the actual project, display the state of release, and record the class at the same time The number of situations, when a certain number of times is reached, an alarm message will be displayed; otherwise, when |I ₀ -I′ _dk |/I′ _dk >α, it will be displayed that the weighing or current sensor is faulty and needs to be repaired.

If I ₀ is closer to I′ _uk , and |I ₀ -I′ _uk |/I′ _uk ≤ α, display the state of putting down, and record the number of times of this kind of situation at the same time, and display an alarm message when a certain number of times is reached; otherwise, when |I ₀ -I′ _uk |/I′ _uk >α, indicating that the weighing or current sensor failure needs to be repaired,

Therefore, it is automatically judged that it is a lifting/lowering operation, and an error is reported in time when the weighing or current sensor fails.

Beneficial effects: the method uses a weighing sensor to detect material weight information, and a current sensor to detect the current information of the driving main hook motor. After the driving terminal software collects multiple sets of samples obtained by the above two sensors, the least square method is used to fit the weight-current estimation model. During the crane lifting process, after performing median average filtering on the detected material weight and the corresponding main hook current, intelligent analysis is carried out according to the weight-current estimation model to obtain accurate driving material weight information and lifting status , and give an alarm in time when the weighing or current sensor fails, improving the accuracy and real-time monitoring of the crane's lifting status, and avoiding the error caused by the hook trial lifting. The automatic identification technology involved in the invention can make up for the randomness and disorder of personnel operating the storage area, and provide a technical basis for realizing fully automatic operation of driving.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the utility model is further described.

Fig. 1 is the software flowchart of the present invention.

Fig. 2 is a schematic top view of the vehicle involved in the present invention.

Fig. 3 is a flow chart of the weight-current model fitting algorithm involved in the present invention.

Fig. 4 is a flow chart of the intelligent analysis subroutine involved in the present invention.

In the figure, there are: driving cart 1, driving trolley 2, driving operator's operating room 3, driving hook 4, weighing sensor 5, and current sensor 6.

Detailed ways

The present invention will be described in further detail below in conjunction with the accompanying drawings.

Fig. 1 shows the data flow chart of the present invention, the method adopts the weighing sensor to detect the material weight information, and the current sensor detects the current information of the driving main hook motor. After the driving terminal software collects multiple sets of samples obtained by the above two sensors, the least square method is used to fit the weight-current estimation model. During the crane lifting process, after performing median average filtering on the detected material weight and the corresponding main hook current, intelligent analysis is carried out according to the weight-current estimation model to obtain accurate driving material weight information and lifting status , and give an alarm in time when the weighing or current sensor fails.

The driving structure involved in the present invention is shown in FIG. 2 . The driving in the material storage area is mainly composed of a driving cart 1, a driving trolley 2, a driving operator's operating room 3, and a driving hook 4. The load cell 5 is installed at the hook, and the current sensor 6 is installed at the main hook motor of the crane. The data of the two sensors are compared with the fitted weight-current estimation model to obtain the lifting/lowering action of the crane. When the crane is lifting materials, the operation of the crane can be automatically determined according to the position information of the cart and trolley, the weight information determined by the present invention, and the driving status information, thereby providing a technical basis for the fully automatic operation of the crane.

Fig. 3 shows the weight-current fitting flow chart of the present invention, in which the material weight and corresponding current are obtained through multiple sampling. The weight-current estimation model I′ _xk =a ₀ W′ _k +a ₁ is obtained by using fitting formulas (1) and (2). For different k, calculate the maximum measurement error ΔI _xk ＝I _xk -I′ _xk , compare the maximum value in |ΔI _xk | and record it as I _xλmax , where x represents the maximum measurement error of the lifting current when x is u When it is d, it means the maximum measurement error of the current. Therefore, for different material weights W _k , the current can be estimated as I′ _xk ±I _xλmax . Specifically, the hoisting current is expressed as I′ _uk ±I _λumax , the lowering current is I′ _dk ±I _λdmax , and the static current in the vertical direction is I′ _sk ±I _λsmax .

${\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; xk</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; xk</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; xk</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; xk</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Among them, N in the formulas (1) and (2) represents the number of sampling times, k represents the number of sampling times, when x of I _xk is u, it represents the lifting current, when x is d, it represents the lowering current, and when x is s, it represents the vertical direction static current.

Fig. 4 shows that the intelligent comparative analysis diagram of the present invention. The material weight W ₀ and the corresponding state current I ₀ are obtained by timing sampling of the sensor data, and the counter 1 is incremented by one to record the total sampling times after a sampling.

If I ₀ ∈ I′ _sk ±I _λsmax , do not process and wait for the next measured value.

If I ₀ ∈I′ _dk ±I _λdmax , the put down state is displayed.

If I ₀ falls within the hoisting current interval I′ _uk ±I _λumax , the hoisting state is displayed.

If I ₀ is not in these two intervals, when I ₀ is closer to I′ _dk , and |I ₀ -I′ _dk |/I′ _dk ≤α, where α is determined according to engineering practice. Display the state of putting down, and add one to the counter 2 to record the number of such situations. When the counter 2/counter 1>m (the value of m is selected according to the actual project needs), the alarm information will be displayed; otherwise, when |I ₀ -I′ _dk | /I′ _dk >α, indicating that the weighing or current sensor is faulty and needs to be repaired.

If I ₀ is closer to I' _uk , and |I ₀ -I' _uk |/I' _uk ≤ α, display the state of putting down, and add one to counter 2 to record the number of times of this type of situation, when counter 2/counter 1 > m (wherein The value of m is selected according to the needs of the actual project), and an alarm message is displayed; otherwise, when |I ₀ -I′ _uk |/I′ _uk >α, it is displayed that the weighing or current sensor is faulty and needs to be repaired.

Therefore, it is automatically judged that it is a lifting/lowering operation, and an error is reported in time when the weighing or current sensor fails.

Combining with the actual situation, Fig. 4 will be further described. When actually lifting 20 tons of material, the main hook current sensor gets 6 different values of 15.10mA, 14.40mA, 14.38mA, 14.42mA, 14.43mA, 14.34mA every 0.5 seconds. Remove 15.10mA and 14.34mA according to the average filter of the median value, and get the average value of 14.41mA, which effectively filters out the occasional pulse current and weakens the influence of actions such as trial hanging. In the weight-current estimation model, the lifting current corresponding to 20 tons is 14.40±0.06mA, and the lowering current is 13.23±0.06mA. By comparison, it is found that the actual measured current 14.41mA is in the lifting current range in the model, and the lifting action is displayed. If the actual measured current is 13.31mA, it is not in the interval between the lifting current and the lowering current. Further judgment knows that it is closer to the lifting current, and the error with the lifting current is less than 10%, it is displayed as a lifting operation, and let Counter 2 is incremented by one. When the value of counter 2 divided by counter 1 (total number of measurements) is greater than m, it indicates that the weighing or current sensor is faulty. If the actual measured current is 11.38mA, it is not in the range between the lifting current and the lowering current, and further judgment knows that it is closer to the lowering current, and the error with the lowering current in the model is greater than 10%, then an alarm will be issued to indicate that the weighing or current sensor is faulty .

Claims (4)

1. a driving handling state detects and method for diagnosing faults automatically, it is characterized in that this method comprises that specifically step is following:

Step 1: under driving handling state automatic detection system off-line state, sling, put down operation,, obtain a plurality of weight of material W through LOAD CELLS and current sensor are gathered with various criterion weight material _kWith the cooresponding electric current I of slinging _Uk, put down electric current I _DkPerhaps steady current I _Sk, utilize method of least square to carry out weight-electric current prediction model that match obtains electric current under weight of material and the different handling states;

Step 2: in the online actual measurement continuous several times electric current and weight information are carried out filtering with median average filter method;

Step 3: through in the actual on-line operation; The interval intelligent comparative analysis of different handling state current under this weight under continual and steady electric current and weight-electric current prediction model during a certain material of handling; Judge that accurately driving is sling, down state, and weighing or current sensor in time reports an error when being out of order;

Step 4: show weight information, handling state, or fault alarm;

In the said online actual measurement process, obtain weight of material information W ₀With current corresponding information I ₀, in weight-electric current prediction model that match obtains with I ₀With W in the model ₀The current corresponding interval compares,

If I ₀∈ I ' _Sk± I _{λ smax}, then do not deal with, wait for observed reading next time,

If I ₀∈ I ' _Dk± I _{λ dmax}, then show down state,

If I ₀Drop on the I ' between Current Zone that slings _Uk± I _{λ umax}, then show the state of slinging,

If I ₀, work as I not in these two intervals ₀More near I ' _Dk, and | I ₀-I ' _Dk|/I ' _Dk≤α, wherein α confirms according to engineering is actual, shows down state, writes down such situation number of times simultaneously, display alarm information when reaching certain number of times; Otherwise work as | I ₀-I ' _Dk|/I ' _Dk＞α, demonstration is weighed or current sensor faults need be overhauled,

If I ₀More near I ' _Uk, and | I ₀-I ' _Uk|/I ' _Uk≤α shows down state, writes down such situation number of times simultaneously, display alarm information when reaching certain number of times; Otherwise work as | I ₀-I ' _Uk|/I ' _Uk＞α, demonstration is weighed or current sensor faults need be overhauled,

Thereby automatically judge it is the operation of slinging/put down, and weighing or current sensor in time reports an error when being out of order;

Wherein, obtaining average current after taking multiple measurements is I ' _Sk, average current and vertical direction quiescent current I _SkMaximum error of measuring be I _{λ smax}, the matched curve and the electric current I of slinging _UkMaximum error of measuring be I _{λ umax}, matched curve with put down electric current I _DkMaximum error of measuring be I _{λ dmax}, weight of material W _kWith the electric current I of slinging _UkObtain I ' through the least square fitting formula fitting _Uk, described weight of material W _kWith put down electric current I _DkObtain I ' through the least square fitting formula fitting _Dk

2. driving handling state according to claim 1 detects and method for diagnosing faults automatically; It is characterized in that N the data of described median average filter method for continuous sampling; Remove a maxim and a minimum value, calculate the center line average values of N-2 data then.

3. driving handling state according to claim 1 detects and method for diagnosing faults automatically, it is characterized in that: described weight of material W _kWith the electric current I of slinging _UkObtain I ' through the least square fitting formula fitting _Uk=a ₀W _k+ a ₁, a wherein ₀And a ₁Be fitting coefficient, the matched curve and the electric current I of slinging _UkMaximum error of measuring be I _{λ umax}, can estimate out at weight of material W _kThe time, the interval I ' of being of the current model of slinging _Uk± I _{λ umax}

$a_0=\frac{\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k^2\right)\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{xk}}\right)-\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k\right)\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k{I}_{\mathrm{xk}}\right)}{N\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k^2-{\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k\right)}^2}---\left(1\right)$

$a_1=\frac{N\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k{I}_{\mathrm{xk}}-\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k\right)\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{xk}}\right)}{N\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k^2-{\left(\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{W}_k\right)}^2}---\left(2\right)$

Wherein N representes sampling number in formula (1), (2), and k representes which time sampling, I _XkX represent the electric current of slinging during for u, x representes to put down electric current during for d, x representes the vertical direction quiescent current during for s.

4. driving handling state according to claim 1 automatically detects and method for diagnosing faults, it is characterized in that: during a certain material of handling, when when vertical direction is static, electric current does not become I basically _Sk, obtaining average current after taking multiple measurements is I ' _Sk, average current and vertical direction quiescent current I _SkMaximum error of measuring be I _{λ smax}, can estimate out vertical direction when static, between its Current Zone I ' _Sk± I _{λ smax}

Images