Methods of predicting failures of in vitro diagnostic instruments. The methods include monitoring with one or more monitoring devices associated with one or more components of the in vitro diagnostic instrument, one or more condition-based maintenance (CBM) parameters of the in vitro diagnostic instrument, providing the one or more condition-based maintenance parameters to a local database, transmitting condition-based maintenance data to a remote service location, storing the condition-based maintenance data at the remote service location, analyzing the condition-based maintenance data according to a failure prediction engine including failure prediction criteria, and performing an action based on predefined deviation from the failure prediction criteria. Apparatus configured to carry out the methods are provided, as are other aspects.

A method of monitoring usage of a component of an aircraft can include: monitoring a usage of the component by using a torque measurement system to calculate torque events in a time period; categorizing the usage of the component by assigning a usage value based upon whether the number of torque events in the period of time is above or below a threshold; and determining a life used of the component.

Sensor and method for performing industrial machinery monitoring based on sensor data processing by a machine learning algorithm. The sensor stores a predictive model of the machine learning algorithm and receives measurements generated by at least one sensing component of the sensor. For example, the measurements comprise one or more of the following: a temperature of an industrial machine, a measurement of a vibration of the industrial machine, and a sound intensity of the industrial machine. The sensor executes the machine learning algorithm, which uses the predictive model for inferring output(s) based on inputs. The output(s) comprise at least one predicted operating condition of the industrial machine (e.g. a predicted failure). The inputs comprise at least some of the measurements. The machine learning algorithm may implement a neural network. The predictive model may be updated based on feedback generated by the sensor or received from another device.

There is provided an information processing apparatus and an information processing method each enabling a transmission apparatus which transmits observation information to be used in prediction to be readily selected from a plurality of transmission apparatuses. A selection section selects a sensor which transmits observation information to be used in prediction as a use sensor from a plurality of sensors on the basis of pieces of information associated with the plurality of sensors, respectively. The present disclosure, for example, can be applied to an information processing apparatus or the like which performs prediction of failure probability of an industrial robot by using observation information which is transmitted from a predetermined sensor of a plurality of sensors installed in an industrial robot.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

A data processing method for processing a plurality of pieces of unit-processing data (each unit-processing data includes a plurality of pieces of time series data) includes: a unit-processing data evaluating step of taking, as reference data, previously defined unit-processing data among the plurality of pieces of unit-processing data and calculating an evaluation value for unit-processing data to be evaluated based on the unit-processing data to be evaluated and the reference data; and a reference data changing step of changing the reference data based on the evaluation value calculated in the unit-processing data evaluating step.

A system that allows a contractor to remotely monitor and/or interact with its customers' building control systems, such as heating, ventilating and air conditioning (HVAC) systems, and analyze information obtained from the building control systems over time. Such a system may help the contractor monitor and diagnosis customer building control systems, setup service calls, achieve better customer relations, create more effective marketing opportunities, as well as other functions. In some cases, the disclosed system may include a controller that analyzes data from HVAC systems, determines a thermal model of a space environmentally controlled by an HVAC system, and provides an energy audit of the space that is environmentally controlled by the HVAC system. The controller may output a result of the energy audit to a user.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

An aggregation layer subsystem, and method of operation thereof, for use with an architect subsystem and a plurality of edge processing devices in a distributed analytics system, wherein each edge processing device is adapted to monitor and control the operation of at least one monitored system according to a first analytic model, the aggregation layer subsystem comprising: a processor and memory, the memory containing instructions which, when executed by the processor, enables the aggregation layer subsystem to: receive a second analytic model from the architect subsystem, the second analytic model based on characteristics of at least one monitored system associated with at least one of the plurality of edge processing devices; receive monitored system information from each of the plurality of edge processing devices; and, provide control signals to the at least one monitored system, via one of the edge processing devices, according to the second analytic model in response to the monitored system information.

Herein provided are methods and systems for detecting failure of a sensor in a control system for a gas turbine engine. A signal is received from the sensor. A high-pass filter is applied to the signal to produce a high-frequency component signal. A rate of occurrence of signal spikes in the high-frequency component signal is determined. The high-frequency component signal is compared to a component signal threshold which is based on at least one known healthy component signal and at least one faulty component signal. The presence of intermittent open circuits caused by the sensor is detected based on the comparing and on the rate of occurrence of signal spikes.