A method for predicting a remaining lifetime parameter of a component installed in a system is provided, in particular of an engine component and/or a filter, the method comprising: repeatedly sensing at least one parameter of the system to obtain a history of data values;

fitting an aging pattern to the data values; and

determining a remaining lifetime parameter of the component from the aging pattern, wherein at least some data values are erased with time such that the fitting is based on a subset of the data values determined since an initialization of the algorithm, wherein data values from an initial phase are not erased but retained as anchor values for the fitting throughout the lifetime determination of the component.

Tool wear monitoring is critical for quality and precision of manufacturing of parts in the machining industry. Existing tool wear monitoring and prediction methods are sensor based, costly and pose challenge in ease of implementation. Embodiments herein provide method and system for monitoring tool wear to estimate Remaining Useful Life (RUL) of a tool in machining is disclosed. The method provides a tool wear model, which combines tool wear physics with data fitting, capture practical considerations of a machining system, which makes the tool wear prediction and estimated RUL more stable, reliable and robust. Further, provides cost effective and practical solution. The disclosed physics based tool wear model for RUL estimation captures privilege of physics of tool wear and easily accessible data from CNC machine to monitor and predict tool wear and RUL of the tool in real-time.

A life expectancy prediction system for a target tool includes a processing machine body, a detector to detect a state data, a learned model storage unit to store learned models generated by executing machine learning using training datasets, including an explanatory variable and an objective variable, the explanatory variable being the state data and the objective variable being a number of first remaining machining times, the learned model storage unit being to store the learned models, each for each of the tools and a remaining machining times prediction unit to select, based on the state data, one learned model and predict a number of second remaining machining times, using the one learned model and the state data.

A remaining life of a robot body is precisely estimated. A robot control apparatus 300 includes: a drive control unit 305 that controls drive of a robot body 200; a detection unit 306 that detects a feature amount quantitatively indicating a deterioration degree of the robot body 200 that is deteriorated over time as the robot body 200 is driven; a determination unit 303 that determinates presence/absence of a sign of malfunction of the robot body 200 based on the feature amount; and an estimation unit 304 that estimates a remaining life of the robot body 200 when presence of a sign of malfunction of the robot body 200 is determined.

A method for predicting a remaining life of a tool of a computer numerical control machine is provided. In the method, indirect measurement indicators of the tool are selected based on monitoring and analyzing a current state of the tool, a prediction model for the remaining life of the tool is established based on data de-noising, feature extraction and a multi-kernel W-LSSVM algorithm. Thereby, a method for predicting a remaining life of a tool of a computer numerical control machine is provided.

The disclosure provides a method to estimate tool life on a cutting machine. A sensor is installed on the cutting machine to measure the cutting thrust of a cutting tool during operation. Specifically, the information gathered from the sensor enables the cutting tool to cut a workpiece at different settings of cutting rate, with a maximum cutting thrust of the cutting tool measured during each cut, and defined as a characteristic signal. Through the characteristic signals obtained from the multiple cuts, factorization processing and inverse processing are initiated to obtain plural values in a complex remaining life index for the cutting tool. Using the cutting rate as the horizontal axis and the remaining life as the vertical axis, the complex remaining life and corresponding cutting rates are plotted on the vertical axis and the horizontal axis to form a line graph illustrating the complex remaining life index.

The invention relates to a transport device (100), in particular for transporting product carriers in a plant for manufacturing food products. The transport device (100) comprises at least one chain (1) to which measuring marks (4) are attached, an electrical drive system (20) for driving the chain (1) with a servo motor (25) and an absolute encoder (21), a plant control unit (30) and a measuring sensor (11) for detecting the measuring marks (4) with a switching frequency of greater than 2000/s. The plant control unit (30) comprises a first input (34) for receiving measurement data from the measuring sensor (11) and a second input (35) for receiving position data from the position sensor (21) and is designed to receive data from the measuring sensor (11) and from the position sensor (21), to mutually correlate said data correctly with respect to time, to determine therefrom lengths between measurement marks (4), more particularly between two consecutive measurement marks, and to produce a signal which provides information about the quality of the chain. A servo position actual value is ascertained by means of oversampling. (FIG. 2)

A diagnostic apparatus (10) diagnoses the existence of an abnormality in a tool for processing of a processing target by each of multiple working machines. The diagnostic apparatus (10) includes an acquirer (110) and a diagnoser (160). The acquirer (110) acquires program identification information identifying a program executed in each of the working machines, tool information indicating a type of the tool applied to processing, and transition information indicating transition of load in the working machine from the start to the end of the processing executed by execution of the program, at execution of the processing in the working machine. The diagnoser (160) diagnoses whether an index value of the load obtained from the transition information is out of a predetermined range corresponding to a combination of the program identification information, the tool information, and machine information identifying the working machine that transmits the transition information.

The present disclosure discloses a control method of industrial Internet of Things (IoT) based on abnormal identification. The IoT includes: an obtaining unit, which is configured to obtain a first machining parameter; a detection unit, which is configured to obtain real-time image data when the first machining parameter is abnormal; an extraction unit, which is configured to obtain a keyframe and obtain a second machining parameter; a judgment unit, which is configured to determine an abnormal cause based on the first machining parameter and the second machining parameter; and a communication unit, which is configured to transmit the abnormal cause to a user terminal through a service platform.

The present disclosure provides various methods for tool condition monitoring, including systems for implementing such monitoring. An exemplary method includes receiving data associated with a process performed on wafers by an integrated circuit manufacturing process tool; and monitoring a condition of the integrated circuit manufacturing process tool using the data. The monitoring includes evaluating the data based on an abnormality identification criterion, an abnormality filtering criterion, and an abnormality threshold to determine whether the data meets an alarm threshold. The method may further include issuing an alarm when the data meets the alarm threshold.