75 results about "Diagnosis problem" patented technology

The invention relates to the field of elevators. In order to early discover and diagnose A elevator fault, the invention adopts the technical scheme that an elevator fault diagnosis and early-warning method based on data drive is achieved by means of a remote service center, a fault diagnosis and prediction terminal and an elevator controller, and the method comprises the steps as follows: firstly, elevator fault data are mined to obtain characteristic information in an elevator fault data stream, and the mined result is stored in an elevator fault case base of the fault diagnosis and prediction terminal; secondly, an elevator fault knowledge base on the fault diagnosis and prediction terminal is updated by the elevator fault case base; thirdly, the case retrieval is carried out on the characteristic of a new elevator fault problem, and the fault diagnosis is carried out on the elevator system by adopting the fault diagnosis method based on the case-base reasoning; and finally information with the characteristic that is most similar with that of the new elevator fault problem is acquired through retrieval of the knowledge or the case in the elevator fault knowledge base to solve the diagnosis problem. The method is mainly suitable for manufacturing and designing image sensors.

Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally.These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation. The deviation between the expected pattern in the process operating data and the actual data pattern are interpreted by fuzzy Petri nets to determine the normality of the process operations. This is then used to help the operator localize and diagnose the root cause of the problem.

Embodiments can provide dynamic assessment by an expert system software (ESS) module. The ESS module may determine a minimum set of diagnostic questions to identify a student's strengths and / or weaknesses for a given subject matter. Each diagnostic question may be a unique question variant generated from a dynamic question template. The ESS module may automatically and dynamically build a personal lesson plan that includes lessons on topics of the subject matter that the student has not mastered. The ESS module may dynamically modify the personal lesson plan to change a lesson and / or add lesson(s) considered by the ESS module as necessary for the student to master the subject matter. Given the ESS module's knowledge on various components of a question such as equation(s) used to produce a correct answer to the question, the ESS module may generate image(s), customized hint(s), customized explanation(s), and / or examine student solutions in step-by-step fashion.

The present invention relates to a method for operation of resonance dislocation image making equipment.Firstly, a dissection canonical model (M) with changeable geometric figure is selected for an examination object depending on diagnosis problems.And then, a plurality of compendia images are measured for areas including the examination object, and different compendia sweep parameters (UP) used for controlling measurement of the compendia images are determined according to the chosen dissection canonical model (M).An object construction (Z) is determined in the sectional view image data (UD) of the measured compendia images, and the canonical model (M) is personalized to match the object construction (Z).According to the chosen canonical model (M) and the diagnosis problems, subsequent dislocation images sweep parameters (SP) for the resonance equipment are selected and controlled, the chosen sweep parameters (SP) are personalized corresponding to the personalized canonical model (M), and a plurality of dislocation images are measured on the basis of the personalized sweep parameter (ISP). The invention also relates to a corresponding image processing system.

The diagnosis problem arises when a system's actual behavior contradicts the expected behavior, thereby exhibiting symptoms (a collection of conflict sets). System diagnosis is then the task of identifying faulty components that are responsible for anomalous behavior. To solve the diagnosis problem, the present invention describes a method for finding the minimal set of faulty components (minimal diagnosis set) that explain the conflict sets. The method includes acts of creating a matrix of the collection of conflict sets, and then creating nodes from the matrix such that each node is a node in a search tree. A determination is made as to whether each node is a leaf node or has any children nodes. If any given node has children nodes, then the node is split until all nodes are leaf nodes. Information gathered from the leaf nodes is used to determine the minimal diagnosis set.

The invention discloses a fault diagnosis method of a photovoltaic diode clamping type three-level inverter. The fault diagnosis method of the photovoltaic inverter combining with a wavelet multi-scale decomposition method and a support vector machine classification algorithm is provided by aiming at a three-level inverter fault diagnosis problem of a photovoltaic micro-grid, taking a system inversion state as an example, and analyzing fault types of various power tubes. The fault diagnosis method adopts the bridge arm voltage signal of the diode clamping type three-level inverter as a research object, and various frequency band energies are extracted as fault characteristic samples by using the wavelet analysis multi-scale decomposition method, and in addition, a multiple valued support vector machine fault classification model is established, and finally, the open circuit fault diagnosis of the three-level inverter power device is completed. The fault diagnosis method is advantageous in that the various fault states of the diode clamping type three-level inverter are obviously discriminated, and the extraction is convenient, and in addition, problems of a conventional data sample diagnosis extraction method such as large data size and tedious process are overcome.

The invention provides an extended constrained polytope set-membership filtering method for a permanent magnet synchronous motor fault diagnosis model. The method comprises the steps of: constructinga nonlinear model equation of a control system of a permanent magnet synchronous motor; implementing a system state model by using a Taylor series to the model equation of the system to implement a linearized equivalent transformation, and using the constrained polytope to define system state variables to construct constrained polytope set operation rules; using dimension reduction and order reduction of the constrained polytope and scale multiplication rule to calculate the amount of system state variable error of the constrained polytope set; realizing an iterative calculation of a constrained polytope set-membership filtering and determining an estimated mean and an estimated variance matrix of the equivalent system state variables by a cross set calculation of the constrained polytope,and completing the iterative calculation. The extended constrained polytope set-membership filtering method has better algorithm effectiveness and computational efficiency, and it can be applied to the fault diagnosis problem of the permanent magnet synchronous motor control system, and realizes the optimal filtering calculation of the model state parameter of the permanent magnet synchronous motor control system.

The invention discloses a multiattribute comprehensive method for structural fault diagnosis of a transformer based on various detection methods. A multiattribute diagnosis model with two-stage structures is built; comprehensive diagnosis is carried out through various detection methods, such as a short circuit impedance method, a frequency response method and a capacitance method. Possible states of the transformer are described through grades. An analytic hierarchy process is introduced to calculate every detection method and the weight coefficients of corresponding criteria. On the basis of the evidential reasoning theory, the comprehensive diagnosis is carried out to the conclusions obtained through the various detection methods; the possible states of the transformer are represented in a probability form. According to the invention, the structural fault diagnosis problem of the transformer under conditions that the original data are missed and various judging conclusions are at variance is solved; the real state of the power transformer can be judged accurately; and the subject factor influence is reduced.

The invention discloses a diagnostic method for simultaneous failure of control system actuator and sensor. Firstly the sensor fault model is converted into an actuator fault model, and the actuator fault diagnosis scheme is modified so as to solve the diagnosis problem of the pseudo actuator fault. Then the sliding mode observer of the pseudo actuator fault diagnosis scheme is combined with the sliding mode observer suitable for the common actuator failure to act on the system when there are actuator and sensor failures in the system simultaneously and the failure diagnosis is completed by analyzing the residual error.