CN113536189B - Method for judging sterilization effect of steam pressure sterilizer - Google Patents
Method for judging sterilization effect of steam pressure sterilizer Download PDFInfo
- Publication number
- CN113536189B CN113536189B CN202010319599.0A CN202010319599A CN113536189B CN 113536189 B CN113536189 B CN 113536189B CN 202010319599 A CN202010319599 A CN 202010319599A CN 113536189 B CN113536189 B CN 113536189B
- Authority
- CN
- China
- Prior art keywords
- data
- sterilization
- pressure
- temperature
- segment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61L—METHODS OR APPARATUS FOR STERILISING MATERIALS OR OBJECTS IN GENERAL; DISINFECTION, STERILISATION OR DEODORISATION OF AIR; CHEMICAL ASPECTS OF BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES; MATERIALS FOR BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES
- A61L2/00—Methods or apparatus for disinfecting or sterilising materials or objects other than foodstuffs or contact lenses; Accessories therefor
- A61L2/02—Methods or apparatus for disinfecting or sterilising materials or objects other than foodstuffs or contact lenses; Accessories therefor using physical phenomena
- A61L2/04—Heat
- A61L2/06—Hot gas
- A61L2/07—Steam
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Epidemiology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Apparatus For Disinfection Or Sterilisation (AREA)
Abstract
The invention belongs to the application field of sterilizers, and particularly relates to a sterilization effect judging method of a steam pressure sterilizer, which aims at a section of data, continuously extracts data of a suspected sterilization process, judges the data, substitutes a special value for the extracted data after judging, and indicates that the data are analyzed until the data of the suspected sterilization process do not exist in a data section; the data extraction method for suspected sterilization process comprises the following steps: searching the highest point of data in the data segment, sequentially searching data turning points in the front and back directions of a time axis at the highest point until a starting point is found, dividing the data segment into a sterilization vacuumizing segment, a high-temperature high-pressure sterilization segment and a temperature pressure drop segment after sterilization is completed according to each turning point, and sequentially judging characteristic labels of the data segment according to the characteristics of three data types after the data segment is divided. The method can be well applied to actual scenes, can identify a plurality of special sterilization abnormal waveforms, and prevents the segment of a complete sterilization process from being misjudged as sterilization abnormal.
Description
Technical Field
The invention belongs to the application field of sterilizers, and particularly relates to a sterilization effect judging method of a steam pressure sterilizer.
Background
National standard of steam pressure sterilizer sterilization process temperature and pressure:
a) In the whole sterilization cycle, the actual measurement value of the sterilization temperature range is not lower than a set value (as shown in the following table) and not higher than the set value of 3 ℃, and the difference value at any 2 points in the sterilization chamber is not more than 2 ℃;
b) The measured pressure range should correspond to the measured temperature range;
c) The actual measurement value of the sterilization time is not lower than the set value and not more than 10% of the set value.
| Temperature/. Degree.C | Shortest sterilization time/min | Relative pressure-kPa |
| 121 | 15 | 103.6 |
| 132 | 4 | 185.4 |
| 134 | 3 | 202.8 |
The temperature and pressure values in the sterilizing chamber are acquired in real time through the temperature and pressure sensors arranged on the sterilizer, a simple threshold judgment algorithm logic rule is too complex and redundant and cannot be well suitable for practical scenes, a plurality of special sterilization abnormal waveforms cannot be identified, and meanwhile, a segment of a complete sterilization process can be misjudged as sterilization abnormality.
Disclosure of Invention
The invention aims to solve the defects and the shortcomings in the prior art, and provides a steam pressure sterilizer sterilization effect judging method which aims at a piece of data, continuously extracts data of a suspected sterilization process, judges the data, replaces the extracted data with a special value after judging, shows that the data are analyzed until the data of the suspected sterilization process do not exist in the data section, can be well suitable for an actual scene, can identify a plurality of special sterilization abnormal waveforms and prevents a segment of a complete sterilization process from being misjudged as sterilization abnormality.
The technical scheme of the invention is as follows: the method for judging the sterilizing effect of the steam pressure sterilizer is characterized in that aiming at a piece of data, the data of a suspected sterilizing process is continuously extracted and judged, and the extracted data is replaced by a special value after judgment, so that the data is analyzed until the data of the suspected sterilizing process does not exist in the data section; the data extraction method for suspected sterilization process comprises the following steps: searching the highest point of data in the data segment, sequentially searching data turning points in the front and back directions of a time axis at the highest point until a starting point is found, dividing the data segment into a sterilization vacuumizing segment, a high-temperature high-pressure sterilization segment and a temperature pressure rising and falling segment after sterilization is completed according to each turning point, and sequentially judging characteristic labels of the data segment according to the characteristics of three data types after the data segment is divided.
Preferably, the logic for determining the signature tag of the three pieces of data is determined as follows:
and (3) aiming at each segmented data segment, obtaining the rising and falling rate of temperature and pressure through fitting, counting the duration time of high temperature/high pressure, further endowing the data segment with a corresponding attribute label, and comprehensively considering the time sequence of the sterilization process, wherein the data segment is sequentially a vacuumizing spike segment, a high-temperature high-pressure sterilization segment and a suspected sterilization cooling and depressurization segment.
Preferably, the sterilization periodicity of the high temperature high pressure sterilization section data is judged, and the process is as follows:
1) If the data has a sterilization period, judging whether the left adjacent data segment has spike characteristics or not, and if the data meets the high-temperature high-pressure period characteristics, indicating that the extracted data is a complete sterilization data segment, further judging whether the sterilization process meets the national standard or not, generating a sterilization event according to data information if the sterilization event does not meet the national standard, generating a sterilization abnormal event if the sterilization event does not meet the national standard, and replacing the extracted whole data segment with a special value after the generation event;
2) If the high-temperature high-pressure sterilization section data has spike characteristics, and the left section data also has spike characteristics, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to the data information if the sterilization process reaches the standard, generating a sterilization abnormal event if the sterilization event does not reach the standard, and replacing the whole section of extracted data with a special value after generating the event;
3) Other characteristics of the high-temperature and high-pressure sterilization section data are meaningless, and the whole section of the extracted data is replaced by a special value.
Preferably, the three pieces of data are characterized as follows:
and (3) sterilizing and vacuumizing: the data rise and fall rapidly, the duration of high temperature and high pressure is short, and the data is like a spike;
high-temperature high-pressure sterilization section: the data rising and falling process is rapid, and the high-temperature high-pressure duration time is long and relatively stable;
a temperature and pressure rising and falling section after sterilization: the data rise and fall rapidly, the high temperature section has longer duration, and the characteristic exists in the sterilization process of part of the sterilization pot.
Preferably, the method for searching the high-temperature high-pressure sterilization section data is as follows: searching the highest point of the data, traversing the data to two sides in sequence, and finding out the left and right endpoints of high temperature/high pressure meeting the numerical value difference within a specified range.
Preferably, the searching method of the data turning points is as follows: and defining a point which is formed by changing the trend of one section of complete sterilization data in a specific direction and can divide a sterilization vacuumizing section, high-temperature high-pressure sterilization data and a complete sterilization section as a turning point, continuously searching left for the left end point of the high-temperature high-pressure data, and recording the point of the trend change.
Preferably, the left end point of the high temperature/high pressure data is compared to the left in the opposite direction of the time axis, the left side of the turning point is in an ascending trend, and the right side is in a descending trend; the right end point of the high-temperature/high-voltage data is compared to the right according to the direction of the time axis, the left side of the turning point is in a descending trend, and the right side is in an ascending trend.
Preferably, the method for judging the rising and falling rates of data is as follows: the data of the high-temperature high-pressure sterilization section can be subdivided into three parts of data rising, high-temperature high-pressure and data falling through turning points and high-temperature high-pressure endpoints, and the rising and falling rates are calculated by adopting a least square method linear fitting method aiming at the data rising and the data falling.
Preferably, the least squares linear fit calculation procedure is as follows:
given a set of data (x i , y i ) I=0, 1,..m-1, fit straight line p (x) =a+bx, mean square error is:
；
in the calculus theory, the minimum value of Q (a, b) is to satisfy:
；
；
sorting into a matrix form:
；
this is called the normal equation for fitting the curve, and the equation is solved by the primordial method or the cramer method:
；
。
aiming at a piece of data, the invention continuously extracts the data of the suspected sterilization process and judges, and after judging, the extracted data is replaced by a special value, which indicates that the data is analyzed until the data of the suspected sterilization process does not exist in the data section, the invention can be well applied to the actual scene, can identify a plurality of special sterilization abnormal waveforms, and prevents the section of the once complete sterilization process from being misjudged as sterilization abnormal.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic representation of experimental data of the present invention.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings, without limiting the scope of the invention.
As shown in fig. 1, a method for determining sterilization in a pressure steam sterilizer is to continuously extract data of a suspected sterilization process for a piece of data, determine the data, and replace the extracted data with a special value (indicating that the data has been analyzed) after the determination until no data of the suspected sterilization process exists in the data piece. The suspected sterilization process extraction method comprises the following steps: searching the highest point of data in the data segment, sequentially searching the data turning points in the front and back directions of a time axis at the highest point until a starting point (the temperature and the pressure value of a normal environment) is found, dividing the data segment into a sterilization vacuumizing segment according to each turning point (the vacuumizing frequency of a complete sterilization process is not fixed and depends on different sterilization standards), a high-temperature high-pressure sterilization segment and a temperature and pressure rising and falling segment after sterilization is finished. And after the data segment is segmented, sequentially judging the characteristic labels of the data segment according to the characteristics of the three data types. The following logic judgment is carried out:
and (3) according to each segmented data segment, obtaining the rate of temperature and pressure rise and fall (the speed of temperature/pressure fall) through fitting, counting the duration of high temperature/high pressure, and further endowing the data segment with corresponding attribute labels (spike, sterilization and suspected sterilization). The time sequence of the sterilization process is comprehensively considered, and the vacuumizing spike section (possibly including multiple sections), the high-temperature high-pressure sterilization section and the suspected sterilization cooling and depressurization section are sequentially adopted.
Firstly, judging the sterilization periodicity of the high temperature and high pressure sterilization section data (the rising and falling are rapid, the high temperature and high pressure are continuous for a certain time and stable)
1) If the data segment has a sterilization period, judging whether the left adjacent data segment has spike characteristics or not, and if the data segment synchronously meets the high-temperature high-pressure period characteristics, indicating that the extracted data is a complete sterilization data segment, further judging whether the sterilization process reaches the national standard (as described in the background art), generating a sterilization event according to data information after reaching standards, generating a sterilization abnormal event after not reaching standards, and replacing the whole extracted data segment with a special value after generating the event;
2) If the high-temperature high-pressure sterilization section data has spike characteristics, and the left section data also has spike characteristics, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to the data information after reaching the standard, generating a sterilization abnormal event after not reaching the standard, and replacing the whole section of extracted data with a special value after generating the event;
3) Other characteristics of the high-temperature and high-pressure sterilization section data are meaningless, and the whole section of the extracted data is replaced by a special value.
Note that: the following are features of the three pieces of data (as shown in fig. 2):
1. and (3) sterilizing and vacuumizing: the data rise and fall rapidly, the duration of high temperature and high pressure is short, and the data is like a spike;
2. high-temperature high-pressure sterilization section: the data rising and falling process is rapid, and the high-temperature high-pressure duration time is long and relatively stable;
3. a temperature and pressure rising and falling section after sterilization: the data rise and fall rapidly, the high temperature section has longer duration, and the characteristic exists in the sterilization process of part of the sterilization pot.
The searching method of the high-temperature data segment comprises the following steps: searching the highest point of the data, traversing the data to two sides in sequence, and finding out the left and right endpoints of high temperature/high pressure meeting the numerical value difference within a specified range.
The searching method of the turning points comprises the following steps: and defining a point which is formed by changing a trend of one section of complete sterilization data in a specific direction and can divide a sterilization vacuumizing section, high-temperature high-pressure sterilization data and a complete sterilization section as a turning point, continuously searching left for the left end point of the high-temperature high-pressure data, and recording the point of the change trend (the left end point of the high-temperature high-pressure data is compared with the left end point of the high-temperature high-pressure data in the opposite direction of a time axis, the left side of the turning point is in an ascending trend, the right side of the turning point is in a descending trend, the right end point of the high-temperature high-pressure data is compared with the right end point of the high-temperature high-pressure data in the direction of the time axis, the left side of the turning point is in a descending trend, and the right side of the turning point is in an ascending trend).
Judging the rising and falling rates of data: the data of the high-temperature high-pressure sterilization section can be subdivided into three parts, namely data ascending, high-temperature high-pressure and data descending, through turning points and high-temperature high-pressure endpoints. For data rising and data falling, the least square method is adopted to linearly fit the rising and falling rates.
The least squares linear fit calculation process is as follows:
given a set of data (x i , y i ）,i = 0,1,...,m-1, fitting a straight line p (x) =a+bx, with a mean square error of
；
In the calculus theory, the minimum value of Q (a, b) is satisfied
；
；
Sorting into a matrix form:
；
this is called the normal equation for fitting the curve, and the equation is solved by the primordial method or the cramer method:
；
。
Claims (7)
1. a method for judging the sterilizing effect of a steam pressure sterilizer is characterized in that: continuously extracting data of a suspected sterilization process aiming at one section of data, judging, and replacing the extracted data with a special value after judging until the data of the suspected sterilization process does not exist in the data section;
the data extraction method for suspected sterilization process comprises the following steps: searching the highest point of data in a data segment, sequentially searching data turning points in the front and back directions of a time axis at the highest point until a starting point is found, dividing the data segment into a sterilization vacuumizing segment, a high-temperature high-pressure sterilization segment and a temperature pressure rising and falling segment after sterilization is finished according to each turning point, and sequentially judging characteristic labels of the data segment according to the characteristics of three data types after the data segment is divided;
the three pieces of data are characterized as follows:
and (3) sterilizing and vacuumizing: the data rise and fall rapidly, the duration of high temperature and high pressure is short, and the data is like a spike;
high-temperature high-pressure sterilization section: the data rising and falling process is rapid, and the high-temperature high-pressure duration time is long and relatively stable;
a temperature and pressure rising and falling section after sterilization: the data rise and fall rapidly, the duration of the high temperature section is longer, and the temperature and pressure rise and fall section after sterilization is completed exists in the sterilization process of part of the sterilization pot;
the data judgment means that: judging whether each piece of data meets the feature definition of the corresponding label, if so, further judging whether the temperature and the pressure of the high-temperature high-pressure sterilization section reach the national standard;
the searching method of the data turning points comprises the following steps: and defining a point which is formed by changing the trend of one section of complete sterilization data in a specific direction and can divide a sterilization vacuumizing section, high-temperature high-pressure sterilization data and a complete sterilization section as a turning point, continuously searching left for the left end point of the high-temperature high-pressure data, and recording the point of the trend change.
2. The method for determining the sterilization effect of a steam pressure sterilizer according to claim 1, wherein: the logic judgment of the feature tag of the three-section data is as follows:
and (3) aiming at each segmented data segment, obtaining the rising and falling rate of temperature and pressure through fitting, counting the duration time of high temperature/high pressure, further endowing the data segment with a corresponding attribute label, and comprehensively considering the time sequence of the sterilization process, wherein the data segment is sequentially a vacuumizing spike segment, a high-temperature high-pressure sterilization segment and a suspected sterilization cooling and depressurization segment.
3. The method for determining the sterilization effect of a steam pressure sterilizer according to claim 2, wherein: judging the sterilization periodicity of the high temperature high pressure sterilization section data, wherein the process is as follows:
if the data has a sterilization period, judging whether the left adjacent data segment has spike characteristics or not, and if the data meets the high-temperature high-pressure period characteristics, indicating that the extracted data is a complete sterilization data segment, further judging whether the sterilization process meets the national standard or not, generating a sterilization event according to data information if the sterilization event does not meet the national standard, generating a sterilization abnormal event if the sterilization event does not meet the national standard, and replacing the extracted whole data segment with a special value after the generation event;
if the high-temperature high-pressure sterilization section data has spike characteristics, and the left section data also has spike characteristics, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to the data information if the sterilization process reaches the standard, generating a sterilization abnormal event if the sterilization event does not reach the standard, and replacing the whole section of extracted data with a special value after generating the event;
other characteristics of the high-temperature and high-pressure sterilization section data are meaningless, and the whole section of the extracted data is replaced by a special value.
4. A method for determining the sterilization effect of a steam pressure sterilizer as claimed in claim 3, wherein: the searching method of the high-temperature high-pressure sterilization section data comprises the following steps: searching the highest point of the data, traversing the data to two sides in sequence, and finding out the left and right endpoints of high temperature/high pressure meeting the numerical value difference within a specified range.
5. A method for determining the sterilization effect of a steam pressure sterilizer as claimed in claim 3, wherein: the left end point of the high-temperature/high-voltage data is compared to the left according to the opposite direction of the time axis, the left side of the turning point is in an ascending trend, and the right side is in a descending trend; the right end point of the high-temperature/high-voltage data is compared to the right according to the direction of the time axis, the left side of the turning point is in a descending trend, and the right side is in an ascending trend.
6. A method for determining the sterilization effect of a steam pressure sterilizer as claimed in claim 3, wherein: the judging method of the data rising and falling rates is as follows: the data of the high-temperature high-pressure sterilization section can be subdivided into three parts of data rising, high-temperature high-pressure and data falling through turning points and high-temperature high-pressure endpoints, and the rising and falling rates are calculated by adopting a least square method linear fitting method aiming at the data rising and the data falling.
7. The method for determining the sterilization effect of a steam pressure sterilizer according to claim 6, wherein: the least squares linear fit calculation process is as follows:
given a set of data (x i , y i ) I=0, 1,..m-1, fit straight line p (x) =a+bx, mean square error is:
；
in the calculus theory, the minimum value of Q (a, b) is to satisfy:
；
；
sorting into a matrix form:
；
this is called the normal equation for fitting the curve, and the equation is solved by the primordial method or the cramer method:
；
。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010319599.0A CN113536189B (en) | 2020-04-22 | 2020-04-22 | Method for judging sterilization effect of steam pressure sterilizer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010319599.0A CN113536189B (en) | 2020-04-22 | 2020-04-22 | Method for judging sterilization effect of steam pressure sterilizer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113536189A CN113536189A (en) | 2021-10-22 |
| CN113536189B true CN113536189B (en) | 2024-02-20 |
Family
ID=78123935
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010319599.0A Active CN113536189B (en) | 2020-04-22 | 2020-04-22 | Method for judging sterilization effect of steam pressure sterilizer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113536189B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4164538A (en) * | 1977-11-11 | 1979-08-14 | American Sterilizer Company | Load conditioning control method for steam sterilization |
| US5466417A (en) * | 1992-11-13 | 1995-11-14 | Tomi Seiko Co., Ltd. | Sterilizer using high temperature steam |
| JPH09266942A (en) * | 1996-02-01 | 1997-10-14 | Miura Co Ltd | Control method for steam sterilizer |
| JP2002119579A (en) * | 2000-10-17 | 2002-04-23 | Miura Co Ltd | Sterilization evaluation method in steam sterilizer |
| JP2004222957A (en) * | 2003-01-23 | 2004-08-12 | Udono Iki:Kk | Steam sterilizer |
| JP2008021732A (en) * | 2006-07-11 | 2008-01-31 | Tokyo Electron Ltd | Method and system for identifying cause of abnormality in pressure, vacuum processing device, and recording medium |
| CN101214385A (en) * | 2008-01-09 | 2008-07-09 | 张让莘 | Control method of safety monitoring system for automatic steam sterilizer |
| JP2014170326A (en) * | 2013-03-01 | 2014-09-18 | Miura Co Ltd | State monitoring device |
| CN204480077U (en) * | 2015-01-21 | 2015-07-15 | 辽宁民康制药有限公司 | A kind of sterilizing data recording and storage device |
| CN106492251A (en) * | 2016-11-22 | 2017-03-15 | 南京巨鲨显示科技有限公司 | A kind of steam sterilization comprehensively indicates to sign |
-
2020
- 2020-04-22 CN CN202010319599.0A patent/CN113536189B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4164538A (en) * | 1977-11-11 | 1979-08-14 | American Sterilizer Company | Load conditioning control method for steam sterilization |
| US5466417A (en) * | 1992-11-13 | 1995-11-14 | Tomi Seiko Co., Ltd. | Sterilizer using high temperature steam |
| JPH09266942A (en) * | 1996-02-01 | 1997-10-14 | Miura Co Ltd | Control method for steam sterilizer |
| JP2002119579A (en) * | 2000-10-17 | 2002-04-23 | Miura Co Ltd | Sterilization evaluation method in steam sterilizer |
| JP2004222957A (en) * | 2003-01-23 | 2004-08-12 | Udono Iki:Kk | Steam sterilizer |
| JP2008021732A (en) * | 2006-07-11 | 2008-01-31 | Tokyo Electron Ltd | Method and system for identifying cause of abnormality in pressure, vacuum processing device, and recording medium |
| CN101214385A (en) * | 2008-01-09 | 2008-07-09 | 张让莘 | Control method of safety monitoring system for automatic steam sterilizer |
| JP2014170326A (en) * | 2013-03-01 | 2014-09-18 | Miura Co Ltd | State monitoring device |
| CN204480077U (en) * | 2015-01-21 | 2015-07-15 | 辽宁民康制药有限公司 | A kind of sterilizing data recording and storage device |
| CN106492251A (en) * | 2016-11-22 | 2017-03-15 | 南京巨鲨显示科技有限公司 | A kind of steam sterilization comprehensively indicates to sign |
Non-Patent Citations (1)
| Title |
|---|
| 高压蒸汽灭菌设备的偏差管理与维修;彭景峰;《设备管理与维修》(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113536189A (en) | 2021-10-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110532674B (en) | A method for measuring the furnace temperature of a coal-fired power station boiler | |
| CN106368813B (en) | A kind of abnormal alarm data detection method based on multivariate time series | |
| CN103761748B (en) | Anomaly detection method and device | |
| CN103631201B (en) | A kind of numerically-controlled machine subsystem reliability Impact analysis method | |
| CN113838054B (en) | Mechanical part surface damage detection method based on artificial intelligence | |
| CN109729091A (en) | An LDoS attack detection method based on multi-feature fusion and CNN algorithm | |
| CN111414703B (en) | Method and device for predicting residual life of rolling bearing | |
| Sainani | Understanding odds ratios | |
| CN113536189B (en) | Method for judging sterilization effect of steam pressure sterilizer | |
| CN115659160A (en) | Data quality measurement method for optimizing digital twin model | |
| CN104008294B (en) | System and method for detecting abnormality of bearing | |
| MX2019010547A (en) | SYSTEM AND METHOD TO DETERMINE FAILURE PATTERNS FROM SENSOR DATA IN THE MANUFACTURING AND VALIDATION PROCESSES OF THE PRODUCT. | |
| CN104517041B (en) | A kind of real-time method for detecting abnormality of chemical engineering data stream based on maximum informational entropy | |
| CN117764771A (en) | K-shape clustering-based offshore wind power output prediction correction method system | |
| CN106021452A (en) | Electromagnetic environment measurement data cleaning method | |
| CN110333078A (en) | A Method for Determining Degradation State Stages of Rolling Bearings | |
| CN106990768B (en) | MKPCA batch process fault monitoring method based on Limited-DTW | |
| CN103364024B (en) | Based on the sensor fault diagnosis method of empirical mode decomposition | |
| CN108519760A (en) | Filament making process steady-state identification method based on variable point detection theory | |
| CN111811818A (en) | Rolling bearing fault diagnosis method based on AP clustering algorithm with specified number of clusters | |
| CN104809662B (en) | The detection method of abnormal data in a kind of Coal Pulverizing System of Thermal Power Plant data | |
| CN104484869B (en) | Image matching method and system for ordinal measure features | |
| CN110766035A (en) | Intelligent detection method for single fault of bearing | |
| CN110530631A (en) | A kind of gear list type fault detection method based on hybrid classifer | |
| CN110207987A (en) | A kind of determination method of rolling bearing performance degeneration decline node |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230116 Address after: Room 704C, No. 3, Lane 88, Shengrong Road, China (Shanghai) Pilot Free Trade Zone, Pudong New Area, Shanghai, 200120 Applicant after: SHIPU INFORMATION TECHNOLOGY (SHANGHAI) Co.,Ltd. Address before: Room 907, building 12, 1818-2, Wenyi West Road, Yuhang street, Yuhang District, Hangzhou City, Zhejiang Province Applicant before: Hangzhou Shipu Technology Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |