An apparatus is described herein related to a humidity-controlled container for perishable items, configured to avoid spoilage by alerting a supervisor to hazardous conditions. Relevant changes in the container environment are detected through various sensors-based event detectors. A timer serves to trigger a plurality of distinct alert states, comprising pre-alerts and full alerts, following an event-initiated time threshold expiry. A data repository of generic merchandise data is accessed in order to derive suitable timer pre-sets in relation to time thresholds for alert states. An account configuration engine serves to customize the threshold presets to the specific items stored inside the container.

An apparatus is described herein related to a humidity-controlled container for perishable items, configured to avoid spoilage by alerting a supervisor to hazardous conditions. Relevant changes in the container environment are detected through various sensors-based event detectors. A timer serves to trigger a plurality of distinct alert states, comprising pre-alerts and full alerts, following an event-initiated time threshold expiry. A data repository of generic merchandise data is accessed in order to derive suitable timer pre-sets in relation to time thresholds for alert states. An account configuration engine serves to customize the threshold presets to the specific items stored inside the container.

The present application discloses a fault report and repair intelligent control system; information regularization module, configured to formulate a time level of the target faulty equipment based on the total operating hours, the operating hours of the current date, and the fault duration; fault regularization module, configured to formulate a fault level of the target faulty equipment based on the fault type; damage regularization module, configured to determine the damage level of the target faulty equipment based on the total number of faults of the target faulty equipment and the time interval from the last fault; intelligent control module, configured to formulate a fault repair strategy for the target faulty equipment. The present application analyzes and classifies the fault conditions of the faulty equipment, by collecting actual fault information, such as the number of faults of the faulty equipment, the fault time, and the fault type, and selects different repair methods according to the analysis result. On the one hand, it improves the overall and effective monitoring of the fault condition of the faulty equipment in the working process, on the other hand, it improves the repairing effect on the faulty equipment, and is beneficial to ensure the maintenance efficiency of the faulty equipment.

A method and a system for controlling a lot risk score based dynamic lot measurement on the basis of equipment reliability index are provided. The method for controlling a measurement, according to an embodiment of the present invention, calculates an equipment reliability index of specific equipment for a specific process in semiconductor manufacturing, calculates a risk score of the specific equipment for the specific process on the basis of an equipment reliability index, and determines, on the basis of the risk score, whether to measure a semiconductor product processed by the specific equipment for the specific process. Therefore, differential quality monitoring and management is possible according to the equipment reliability index, a measuring instrument can be efficiently used, quality and yield can be improved through timely measurement, and management convenience can be increased through automatic and dynamic lot measurement control.

The embodiments herein disclose a system and method for remote and non-intrusive monitoring of a plurality of assets and entities. The system comprises a central platform module, a client module, a plurality of sensor units and a plurality of entity modules. Each sensor device is configured for measuring vibrations and changes in orientation of physical structure in three-dimensional axis to generate sensor data. The system also comprises a machine learning module, which provides the system with the intelligence to extract relevance of a measured physical parameter to contextually provide actions to be taken. The embodiments herein also provide a system and method for remote and non-intrusive monitoring of a level of liquid fuel in an electric power generator device. The system comprises no intrusive mechanism to measure the actual level of liquid present in the electric power generator.

According to an embodiment, an accounting apparatus includes a change machine, a detection device, and a controller. The controller determines whether or not the change machine is capable of paying change in cash on the basis of a result of a detection by the detection device. The controller accepts, when determining that the change machine fails to pay the change in cash, a payment method selected by a customer from a plurality of change payment methods other than paying in cash.

A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a plurality of times within a first time period; determine a first time at which a first cumulative sum value is at a first maximum; identify a second cumulative sum value at a second maximum at a second time occurring after the first time; compare the identified second cumulative sum value to a threshold; and based on determining that the identified second cumulative sum value does not exceed the threshold, determine that a first fault ended at the first time.

Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.

There is described a zone controller and method for identifying a root cause failure at a zone. The zone controller determines whether a temperature measurement deviates from a temperature setpoint of the temperature sensor, and generates a first repair code, a second repair code, and/or a third repair code. The first repair code replaces a temperature sensor in response to detecting that a reading of the temperature sensor has failed. The second repair code releases an operator override on the reading of the temperature sensor in response to detecting that the reading of the temperature sensor has been overridden. The third repair code releases an operator override on a setpoint of the temperature sensor in response to detecting that the setpoint of the temperature sensor is outside the predetermined setpoint range. One or more of these repair codes are provided to a remote device.

A system and method for data collection and frequency analysis with self-organization functionality includes analyzing with a processor a plurality of sensor inputs, sampling with the processor data received from at least one of the plurality of sensor inputs at a first frequency, and self-organizing with the processor a selection operation of the plurality of sensor inputs.