A malfunction determination method for a production machine including a motor as a driving source of a rotating mechanism acquires sensor data of a sensor for detecting a condition of the production machine, determines whether the production machine has an operation stop period during which the production machine has stopped its operation for a predetermined period of time or longer in accordance with an operation history of the production machine, sets a malfunction determination suspension period for suspending a malfunction determination of the production machine when determined to have the operation stop period, in accordance with a length of the operation stop period, and determines whether the production machine has a malfunction in a period other than the malfunction determination suspension period.

A monitoring apparatus may include reception logic operable to receive processing characteristic data generated during the processing of the effluent stream; segregation logic operable to segregate the processing characteristic data into contributing processing characteristic data associated with contributing periods which contribute to a condition of the at least one processing tool and non-contributing processing characteristic data associated with non-contributing periods which fail to contribute to the condition; and fault logic operable to utilise the contributing processing characteristic data and to exclude the non-contributing processing characteristic data when determining a status of the condition.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. To this end, a server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.

Monitoring indoor environmental conditions is provided. Sensor data and its corresponding time stamps from is collect from a number of environmental sensors within an enclosed environment. A set of all possible states is defined for a specified time period, each state representing a range of sensor data values. A probability of the system changing from any one state to another is modeled according to a Markov chain. When a new sensor data value is received from a sensor it is compared to a last sensor data value of a previous state, and a probability of transition from the previous state to the current state is determined. If the probability of transition from the previous state to the current state is less than a predetermined threshold, an anomaly is detected, and a service request is generated.

A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Methods and systems for enhancing the functionality of a semi-autonomous vehicle are described herein. The semi-autonomous vehicle may receive a communication from a fully autonomous vehicle within a threshold distance of the semi-autonomous vehicle. If the vehicles are travelling on the same route or the same portion of a route, the semi-autonomous vehicle may navigate to a location behind the fully autonomous vehicle. Then the semi-autonomous vehicle may operate autonomously by replicating one or more functions performed by the fully autonomous vehicle. The functions and/or maneuvers performed by the fully autonomous vehicle may be detected via sensors in the semi-autonomous vehicle and/or may be identified by communicating with the fully autonomous vehicle to receive indications of upcoming maneuvers. In this manner, the semi-autonomous vehicle may act as a fully autonomous vehicle.

The present invention provides a modeling method for a smart prognostics and health management system. The method comprises a new tree establishing step, a dual-branch modeling step, and a model adaptive optimization step. As monitoring data increases, a golden model can be selected as a benchmark for optimization decision from prediction hypothesis models constructed by the dual-branch modeling step. This benchmark is used for next prediction. A prediction result of the system is caused to meet an expected target value. The present invention provides a computer program product for the smart prognostics and health management system at the same time. The above-described modeling method for the smart prognostics and health management system is completed when the computer program product is executed.

According to some embodiments, system and methods are provided, comprising receiving, at a machine health module, non-parametric data associated with operation of an installed product; generating, via the machine health module, a health status for at least one of a failure type and a remaining useful life of the installed product, based on the received non-parametric data; and generating an operating response of the installed product based on the generated health status. Numerous other aspects are provided.

Continuous monitoring of a model in an interactive computer simulation station. The model comprises a plurality of interrelated parameters defining a dynamic behavior of a simulated interactive object in an interactive computer simulation when inputs are provided on tangible instrument(s) of the station. During a diagnostic period of time, a frequency sweep of the model is performed for measuring the dynamic behavior of the simulated interactive object. During the frequency sweep, each of the tangible instrument(s) is automatically mechanically moved following an input function defining an input range variation at a related frequency. The frequency sweep provides an actual frequency response function for the tangible instrument(s) defining the dynamic behavior. The station is determined to require maintenance when the dynamic behavior of the simulated interactive object, measured by the frequency sweep, is outside of a target dynamic behavior range for the simulated interactive object.

A method includes obtaining data associated with operation of an aircraft and determining a first operational phase of the aircraft based on the data. The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data satisfying a first condition associated with the candidate operational phase, the first portion of the data corresponding to a first time. The method includes evaluating a second portion of the data based on a second condition associated with the candidate operational phase, the second portion of the data corresponding to a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication that indicates an occurrence of an operational phase transition to the candidate operational phase at the first time.