Patent Application
20190278527
This application relates generally to maintenance of document processing devices. The application relates more particularly to predicting device failures for multifunction peripherals to facilitate prophylactic device repair and part availability.
In an example embodiment a system device failure prediction includes a processor, memory and a network interface. The system receives device status data from each of a plurality of identified multifunction peripherals. Service history data for each of the multifunction peripherals is stored in memory. The processor detects anomalies in received device status data and generates predictive device failure data for at least one identified multifunction peripheral in accordance with detected anomalies and service history data.
In accordance with another example embodiment, device failure prediction is used to generate or optimize scheduling of device servicing.
Document processing devices include printers, copiers, scanners and e-mail gateways. More recently, devices employing two or more of these functions are found in office environments. These devices are referred to as multifunction peripherals (MFPs) or multifunction devices (MFDs). As used herein, MFP means any of the forgoing.
Given the expense in obtaining and maintain MFPs, MFPs are frequently shared by users and monitored by technicians via a data network for example using Simple Network Management Protocol (SNMP). MFPs are complex devices that are subject to failures. MFP failures are frustrating for device users and work against a manufacturer's reputation. They can result in periods when a MFP is out of service, leaving users without a powerful office tool and causing user frustration when a job must wait or an alternative MFP used, such as one that is not conveniently located or one without needed capabilities that were available on the out of service MFP.
Not only are failed devices a burden on end users, they can provide significant financial cost to MFP providers. A common business model for MFPs is one wherein a distributor enters into an end user agreement where the distributer provides a device, at little or no upfront cost to the end user. User charges are based a cost per page. This cost reflects device usage charges, as well as maintenance costs. If a device fails, the end user must make a service call, and the distributor must dispatch a technician to fix the MFP. Significant human resource costs are associated with receiving a service call, logging a call, scheduling a service time, dispatching a service technician, and diagnosing and repairing the device. Such service costs can lower the distributor's profitability, increase the end user's cost per page, or both.
Various embodiment will become better understood with regard to the following description, appended claims and accompanying drawings wherein:
The systems and methods disclosed herein are described in detail by way of examples and with reference to the figures. It will be appreciated that modifications to disclosed and described examples, arrangements, configurations, components, elements, apparatuses, devices methods, systems, etc. can suitably be made and may be desired for a specific application. In this disclosure, any identification of specific techniques, arrangements, etc. are either related to a specific example presented or are merely a general description of such a technique, arrangement, etc. Identifications of specific details or examples are not intended to be, and should not be, construed as mandatory or limiting unless specifically designated as such.
In accordance with an example embodiment disclosed herein, a network interface receives device status data from each of a plurality of identified multifunction peripherals. The system includes a processor and associated memory. The processor detects anomalies in received device status data, generates predictive device failure data for at least one identified multifunction peripheral in accordance with detected anomalies, generates a failure window corresponding an anticipated timing of a device failure associated with the predictive device failure data, and outputs the predictive device failure data via the network.
In accordance with a more limited example embodiment, the memory stores a device service schedule for the plurality of multifunction peripherals. The processor generates an updated device service schedule in accordance with the predictive device failure data and the failure window and output the updated device service schedule to a device service provider via the network interface.
In accordance with another more limited example embodiment, the processor generates the service schedule to include servicing a multifunction peripheral associated with a predicted failure in advance of the failure window.
In accordance with another more limited example embodiment, the processor generates the service schedule so as to balance service loads among a plurality of service technicians.
In accordance with another more limited example embodiment, the memory storesa plurality of device service procedures. The processor identifies a device service procedure corresponding to the predictive device failure, and outputs an identified device service procedure.
In accordance with another example embodiment, a system includes a plurality of multifunction peripherals. Each multifunction peripheral includes a plurality of sensors that generate state data corresponding to a state of an associated multifunction peripheral. Each multifunction peripheral further includes an intelligent controller and a network interface. The intelligent communicates generated state data to an associated server via the network interface. The server includes a server includes a network interface that receives device state data from each of the plurality of identified multifunction peripherals, a processor and associated memory. The memory stores service history data for each of the multifunction peripherals. The processor detects anomalies in received device state data. The memory also stores location data corresponding to a location of each of the plurality of multifunction peripherals. The processor generates predictive device failure data for subset of the multifunction peripherals in accordance with detected anomalies and service history data, identifies a device cluster within the subset of multifunction peripherals in accordance with the location data, and outputs the predictive device failure data and device location corresponding to identified multifunction peripherals in the device cluster.
In accordance with a more limited example embodiment, the memory also stores a maintenance schedule for the plurality of multifunction peripherals. The processor generates an updated maintenance schedule in accordance with the device cluster.
Human resource expense is the highest cost of most organizations. This is true for service orientated organization that must keep mechanical machines running to receive revenue. In general, service organizations are reactive organizations, meaning they wait until a customer calls notifying them an MFP is broken. This reactive approach creates an unpredictable schedule of how many service technicians are needed during a week or month.
Turning to
Server 116 accumulates MFP data such as device error logs, device usage, such as number of print jobs or device page count, mechanical wear and tear tracking, forced shutdowns, copy interruptions or environmental factors such as temperature, humidity, ground stability, barometric pressure, and the like. Server 116 uses its available information to predict likely device failures in advance of an actual failure. Server 116 further includes information on one or more available service technicians, along with their locations and workloads. From this information, server 116 determines which technician is best suited for servicing devices, or clusters of devices. This information can be communicated to a service center or service technician via a digital device, such as tablet computer 120 of service technician 124.
Turning now to
Processor 202 is also in data communication with a storage interface 208 for reading or writing to a storage 216, suitably comprised of a hard disk, optical disk, solid-state disk, cloud-based storage, or any other suitable data storage as will be appreciated by one of ordinary skill in the art.
Processor 202 is also in data communication with a network interface 210 which provides an interface to a network interface controller (NIC) 214, which in turn provides a data path to any suitable wired or physical network connection 220, or to a wireless data connection via wireless network interface 218. Example wireless connections include cellular, Wi-Fi, Bluetooth, NFC, wireless universal serial bus (wireless USB), satellite, and the like. Example wired interfaces include Ethernet, USB, IEEE 1394 (FireWire), Lightning, telephone line, or the like. Processor 202 is also in data communication with user interface 219 for interfacing with displays, keyboards, touchscreens, mice, trackballs and the like.
Processor 202 can also be in data communication with any suitable user input/output (I/O) interface 219 which provides data communication with user peripherals, such as displays, keyboards, mice, track balls, touch screens, or the like.
Also in data communication with data bus 212 is a document processor interface 222 suitable for data communication with MFP functional units. In the illustrated example, these units include copy hardware 240, scan hardware 242, print hardware 244 and fax hardware 246 which together comprise MFP functional hardware 250. It will be understood that functional units are suitably comprised of intelligent units, including any suitable hardware or software platform.
Turning now to
Device management system 404 provides device state information 408 for application of machine learning and analysis for predictive device failures by a suitable machine learning platform 412 such as Microsoft Azure. Additional information 416 for such prediction, such as device service log information, is provided by a suitable CMMS (Computerized Maintenance Management System (or Software)) 420, and is sometimes referred to as Enterprise Asset Management (EAM). By way of particular example a CMMS system 420 can be based on CMMS Software, Field Service Software, or Field Force Automation Software provided by Tessaract Corporation.
A database anomaly is a flaw in database. Human error can generate anomalies which occurs because of poor planning and storing everything in a flat database. Anomalies can be removed by the process of normalization which is suitably performed by splitting/joining of tables.
There are three types of database anomalies:
By way of particular example, a determination of likeliness of a forthcoming service call can be utilized to schedule device maintenance. Such scheduling is suitably integrated with service calls already scheduled or with servicing of two or more geographically proximate devices to minimize travel time needed for technician on-site visits. Suitable machine learning systems are built on available third party platforms such as R-Script, Microsoft Azure, Google Next, Kaggle.com or the like.
While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the spirit and scope of the inventions.
