299 results about "Machine failure" patented technology

A prognostic processor for predicting machine failure in avionics electronics comprises prognostic capabilities in a single integrated circuit, with a processor, volatile and non-volatile memory, clock, on-chip and off-chip sensors and transducers, A/D converters, a common I/O interface adapted to be employed in a network of similar prognostic processors, and predictive Failure Analysis (FA) model software, which may be distributed throughout the network. The FA software employs a log file history, with the log file history storing data collected by the prognostic processor, real-time execution of a predictive model, with the ability to update the FA model with data from field failures. The prognostic processor network supports hierarchical processing to work with multiple prognostic processors. The prognostic processor system is applicable to FA monitoring of a wide range of avionics electronic equipment, in particular, Line Replacement Units (LRUs).

The present invention discloses an integrated support vector machine mixed intelligent diagnosis method of machine failure. Said method includes the following steps: respectively adopting and lifting small wave packet, utilizing frequency band and empirical mode decomposition process to decompose vibration signal according to the eigen mode component and extract time domain statistical character of decomposed signal to form total characteristic set; providing characteristic distance evaluation technique and characteristic evaluation index; utilizing said characteristic evaluation index to select most sensitive characteristic set from the total characteristic set; using said most sensitive characteristic set as diagnosis characteristics and creating integrated support vector machine mixed intelligent diagnosis model so as to implement intelligent diagnosis of machine failure state.

The invention discloses a washing machine malfunction warning and judging method, a washing machine and a washing machine malfunction warning and judging system. The washing machine failure warning and judging method includes the steps that when a malfunction occurs, the washing machine sends malfunction relevant information to a specified communication terminal, and the communication terminal analyzes and judges whether the malfunction needs to be eliminated by a user according to the received malfunction relevant information. The washing machine comprises a washing machine body and a communication module, and message passing can be conducted between the communication module and a communication terminal of the user. The washing machine malfunction warning and judging system comprises the communication terminal and the washing machine, and communication models which are mutually matched are arranged in the communication terminal and the washing machine respectively. The washing machine malfunction warning and judging method and the washing machine malfunction warning and judging system can solve an occurred malfunction problem within shortest time, improve user satisfaction degree, feed the malfunction problem back to a design source, and prevent the problem from happening again.

A system and methodology for continuous condition monitoring of rotating equipment employ adaptive signal processing techniques to determine the RPM of a rotating machine from the time-based vibration data. RPM is determined based upon the input of a digitized time-based sample sequence of vibration data acquired directly from a vibration transducer for on-line real-time measurement of the machine RPM. Once RPM is determined, online vibration analysis for a given RPM or set of RPMs may be performed. The present invention extracts characteristic vibration features from vibration data and uses these extracted values to provide condition detection and diagnoses of machine faults.

A method and system of on spot diagnosis of one or more issues associated with a machine includes collecting sound data and image data associated with a machine through a mobile device and transmitting them to a cloud server over a communications network. The on spot diagnosis includes analyzing the sound data and image data in combination with an on spot feedback system through the cloud server. The on spot feedback system is communicatively coupled to a machine-learning engine and a Big Data architecture. The machine issue condition is indicated through a user interface dynamic such a circular gauge. An alarm is set, through a mobile device, for the machine issue.

The present invention relates to a new type protection device for shredders which comprises a paper number detecting device mounted at the paper inserting passage of the shredder, a warning device mounted on the shredder and a controlling device in line connection with the paper number detecting device and the warning device respectively, mounted in the shredder and in line connection with the driving component of the shredder driving the shredder blades, preferably the protection device further comprises an insert detecting device in line connection with the controlling device and mounted at the paper inserting passage, and at least one opaque shielding component mounted at the paper inserting passage and at the middle of which there is at least one slot along the length direction, the present invention controls the number of the paper to be shredded during the paper inserting stage, and shreds paper automatically only in a predetermined value range, to avoid abnormalities that result in failure of the machine such as jam and tooth breaking when the shredder is shredding many pieces of paper, so as to reduce maintenance times and cost, extend service life, and make application of shredders safer and more reliable, more convenient and simpler, then to achieve better protection of shredders.

The present invention discloses a flat-belt driven disc separator, comprising a power part, a driving part, a lubrication system, a working part, a machine part and a control and assisting part. In addition to that the requirements of the function and performance of the separator is satisfied, the driving system is largely simplified, the driving path is brief, the quantity of driving components is largely reduced and the technical requirement of processing is lowered; regular replacement of expensive copper gear wheel, friction drum and other wearing parts is avoided, and as a result, the structure of the complete machine is simplified, the cost of production and use is reduced, the failure rate of the machine is lowered, and the technical performance of the complete machine is improved. The invention can be directly applied to condensing separation of natural rubber, and the invention can also be applied to separating other emulsions; the rotary-drum structure of the invention can overcome the disadvantages of the prior art; the continuous operation time is prolonged and the rotary-drum heating is alleviated; the strength, safety, reliability and maintenance technique are improved. The driving part and the lower machine body can form the driving of a manual slag-discharge separator, a gushing slag-discharge separator and a ring-valve slag-discharge separator and the lower machine body.

The invention relates to an electronic complete machine storage life assessment and prediction method. On the basis of analyzing a typical complete machine failure mechanism, an Arrhenius model-basedcomplete machine level acceleration factor calculation method is proposed; the method can perform electronic complete machine acceleration factor calculation by only three parameters; an accelerationtest is performed according to a calculation result and data is obtained; by utilizing an optimized GM(1,1) grey model-based prediction algorithm, a degradation law of natural storage and accelerationtest data is researched; the consistency of degradation trends of two groups of data is analyzed; closed-loop correction of acceleration factor calculation model parameters is realized by utilizing aconsistency analysis result; and finally electronic complete machine storage life prediction is realized by utilizing a corrected model. The method is simple and easy to understand and relatively high in prediction precision, and is suitable for engineering popularization.

Invention concerns a machine fault diagnostic system. Sensors are provided for sensing an operational parameter of the machine over time in use and outputting corresponding sensor signals. A machine operation diagnosis tool is arranged to receive the sensor signals and has a data store for operational parameter data indicative of a normal mode of machine operation and operational parameter data indicative of one or more known machine faults. The diagnosis tool has one or more processor arranged to compare the received sensor signals with the operational parameter data in the data store in order to determine a match with either the normal mode of operation or the one or more known machine fault, and wherein the processor sentences an unknown fault in the event that said match is not established. The system may record unknown faults and update the diagnosis tool as unknown faults become recognizable faults.

An active noise cancellation fault detector (NCFD) is presented that is configured to cancel normal machine noise in an operator cabin, while allowing noise associated with a machine failure mode to remain so that an operator can hear when a machine component is beginning to fail or is in a failure mode. An NCFD can generate a model waveform of normal noise scaled to machine operating parameters. The model waveform can be inverted and adjusted in gain and phase to provide a cancellation waveform. The invention can be configured to determine operator location within a machine cab so that cancellation signals can be configured with the proper phases to destructively interfere with normal machine noise within a quiet zone or noise suppression envelope around an operator. Noise within the quiet zone can be detected and compared to various fault waveforms to determine whether a machine component is beginning to fail.

A method and apparatus for detecting problems in a rotating machine is disclosed. The method and apparatus include creating a definition of the rotating machine using a plurality of templates that specify components and functional characteristics of the components. In addition, the method and apparatus include identifying a plurality of sensors configured to provide operational data related to the rotating machine and defining a plurality of problem rules including logical expressions by which machine faults are deduced from specified portions of the operational data. Further, the method and apparatus include performing an analysis of operation of the rotating machine, the analysis including acquiring actual operational data from the plurality of sensors and applying the problem rules and templates to the operational data to detect faults in the rotating machine and in response thereto generating an indication when a fault is detected.

The invention belongs to the technical field of real-time abnormity detection, and specially relates to a real-time energy consumption abnormity detection method with college building structure characteristics being combined. Energy consumption modes of buildings are identified in a self-adaptive manner through a SA-DBSCAN algorithm, and a decision tree of the energy consumption modes is constructed through a C4.5 algorithm; if an acquisition child node has no machine fault, real-time energy consumption is processed first according to change of a parent-child structure of a building node, then, the type of the real-time energy consumption is obtained according to the decision tree, and abnormity is detected through an LOF algorithm; if abnormity is detected, the decision tree is adjusted based on whether a boundary threshold value is reached; and normal energy consumption data increment is updated to the energy consumption modes of the buildings in batch, and an abnormity detection model is adjusted dynamically according to whether the type of energy consumption mode is changed. The method can effectively detect abnormal energy consumption and dynamically adjust the detection model to reduce misjudgment.

The invention discloses a control method of integral saturation resistance in the auxiliary machine failure load reduction process, which is characterized in that a RB (Radio Beacon) signal is not adopted to trigger; two amplitude limiters are additionally arranged in front of a hand-operated device; triggering is carried out through comparing a command with the output of an auxiliary machine; when an auxiliary machine at the other side trips or fails, and the auxiliary machine at the side is in an automatic state; the output at the side is suddenly increased; within the scope allowed by the output of the auxiliary machine at the side, the integral saturation resistance action is not generated; and if the output at the side is exceeded, an integral saturation resistance function is generated till the integral saturation is eliminated. The control method has the beneficial effects that when an auxiliary machine of a thermal power unit fails, the load can be quickly and effectively reduced; within a period of time before reset in the RB process, if the auxiliary machine fails or parameters are improperly set, the integral saturation resistance function can also be generated through the design; and when the output of the auxiliary machine is larger and is lower than the maximum output of the auxiliary machine at the single side, the RB signal cannot trigger a controller to track, so that the oscillation of a system is prevented; and the safe operation of the auxiliary machine is favorably maintained.

Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a machine failure history; ascertaining a basic weight range for each of the plurality of basic memory depth values according to a pre-stored table including a plurality of mappings each mapping between a basic memory depth value and a basic weight range, or a predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value; ascertaining a composite initial weight range by calculating an average weight range of the ascertained basic weight range for each identified basic memory depth value; generating initial weights based on the composite initial weight range; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the generated initial weights.

A method and system for predictive and conditional fault detection that utilizes a machine's characteristics and sensor detected faults to predict and diagnose future faults. The fault detection method utilizes machine characteristics and fault sensors on the machines to generate extracted vectors. The two types of vectors are combined into an extracted vector. The extracted vector is stored in a machine state database and a fault symptom database. The databases utilize this information for future machine condition evaluation and maintenance suggestions. The information in the databases is mined to provide optimal fault detection suggestions by comparing vectors from the databases. Additional fault inspections, machine fault information, and comparisons between machine vectors and fault vectors further refine the fault vectors for optimal diagnoses. The resultant fault detection generates additional useful fault information, which is added to the database to further refine the fault detection method and system.

The present invention discloses a system and method for vibration analysis and phase analysis of vibration waveforms using dynamic statistical averaging of tachometer data to accurately calculate rotational speed. Vibration analysis is performed on a machine having a variable frequency drive by using a tachometer to monitor rotational speed of the drive shaft and a logic device to calculate speed parameters associated with the drive shaft using the tachometer data. The speed parameters include a maximum speed, a minimum speed, and an average speed of the drive shaft. By correlating the vibration spectra of the motor drive with the speed parameters, machine faults can be identified based upon the energy distribution in the spectra. Further, vibration waveforms from two or more locations on the machine can be sequentially acquired through synchronous triggering by using a pulse edge of a stable tachometer signal. The waveforms can be compared to determine a phase difference to help in identifying any machine faults that may be present.